Search

The utilization of optical and synthetic aperture radar (SAR) multi-source data to obtain better land classification results has received increasing research attention. However, there is a large property and distributional difference between optical and SAR data, resulting in an enormous challenge to fuse the inherent correlation information to better characterize land features. Additionally, scale differences in various features in remote sensing images also influence the classification results. To this end, an optical and SAR Siamese semantic segmentation network, OPT-SAR-MS2Net, is proposed. This network can intelligently learn effective multi-source features and realize end-to-end interpretation of multi-source data. Firstly, the Siamese network is used to extract features from optical and SAR images in different channels. In order to fuse the complementary information, the multi-source feature fusion module fuses the cross-modal heterogeneous remote sensing information from both high and low levels. To adapt to the multi-scale features of the land object, the multi-scale feature-sensing module generates multiple information perception fields. This enhances the network's capability to learn contextual information. The experimental results obtained using WHU-OPT-SAR demonstrate that our method outperforms the state of the art, with an mIoU of 45.2% and an OA of 84.3%. These values are 2.3% and 2.6% better than those achieved by the most recent method, MCANet, respectively. [ABSTRACT FROM AUTHOR]

The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system. [ABSTRACT FROM AUTHOR]

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm's performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model. [ABSTRACT FROM AUTHOR]

A space-borne remote sensing method was applied, validated, and demonstrated in a case study on the Samuel de Champlain Bridge in Montreal, Canada. High-resolution C-band radar satellite imagery was analyzed using the Persistent Scatterer Interferometric Synthetic Aperture Radar technique to derive bridge displacements and compare them against theoretical estimates. Multispectral and long-wave thermal infrared satellite imagery acquired during the InSAR observation period and historical environmental data were analyzed to provide context for the interpretation and understanding of InSAR results. Thermal deformation measurements compared well with their theoretical estimates based on known bridge geometry and ambient temperature data. Non-thermal deformation measurements gave no evidence of settlement during the 2-year monitoring period, as would normally be expected for a newly constructed bridge with its foundation on bedrock. The availability of environmental data obtained from multispectral and thermal infrared satellite imagery was found to be useful in providing context for the bridge stability assessment. Ambient temperature measurements from thermal infrared satellite imagery were found to be a suitable alternative in cases where data from in situ temperature sensors or nearby weather stations are not available or not fit for purpose. No strong correlation was found between the river conditions and bridge deformation results from the InSAR analysis; this is partly due to the fact that most of these effects act along the river flow in the north–south direction, to which the satellite sensor is not sensitive. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to detect the coastal erosion and progradation effects in the 'Atrato River' delta, located in the Gulf of Urabá in Colombia, using SAR (Synthetic Aperture Radar) images. Erosion is the physical–mechanical loss of the soil that affects its functions and ecosystem services while producing a reduction in its productive capacity. Progradation is the deposition of layers in the basinward direction while moving coastward. Other studies have investigated these two phenomena using optical images, encountering difficulties due to the persistent presence of clouds in this region. In order to avoid the cloud effects, in this study, we used 16 Sentinel 1 SAR images with two different polarizations between 2016 and 2023. First, each image was rescaled from 0 to 255, then the image was despeckled by a deep learning (DL) model. Afterwards, a single RGB image was composed with the filtered polarizations. Next, a classifier with 99% accuracy based on Otsu's method was used to determine whether each pixel was water or not. Then, the classified image was registered to a reference one using Oriented FAST and Rotated BRIEF (ORB) descriptor. Finally, a multitemporal analysis was performed by comparing every image to the previous one to identify the studied phenomena, calculating areas. Also, all images were integrated to obtain a heatmap that showed the overall changes across eight years (2016–2023) in a single image. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. In addition, a comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons. [ABSTRACT FROM AUTHOR]

In the face of increasing flood risks intensified by climate change, accurate flood inundation mapping is pivotal for effective disaster management. This study introduces a novel explainable deep learning architecture designed to generate precise flood inundation maps from diverse satellite data sources. A comprehensive evaluation of the proposed model is conducted, comparing it with state-of-the-art models across various fusion configurations of Multispectral Optical and Synthetic Aperture Radar (SAR) images. The proposed model consistently outperforms other models across both Sentinel-1 and Sentinel-2 images, achieving an Intersection Over Union (IOU) of 0.5862 and 0.7031, respectively. Furthermore, analysis of the different fusion combinations reveals that the use of Sentinel-1 in combination with RGB, NIR, and SWIR achieves the highest IOU of 0.7053 and that the inclusion of the SWIR band has the greatest positive impact on the results. Gradient-weighted class activation mapping is employed to provide insights into its decision-making processes, enhancing transparency and interpretability. This research contributes significantly to the field of flood inundation mapping, offering an efficient model suitable for diverse applications. This study not only advances flood inundation mapping but also provides a valuable tool for improved understanding of deep learning decision-making in this area, ultimately contributing to improved disaster management strategies. [ABSTRACT FROM AUTHOR]

The radius of maximum wind Rmax, an important parameter in tropical cyclone (TC) ocean surface wind structure, is currently resolved by only a few sensors so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semiempirical model relying on an outer wind radius, intensity, and latitude was fit to best-track data. In this study we revise this semiempirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, Rmax observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an intercalibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010–20 and yields Rmax reanalyses and trends that are more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model's physical basis, which further shows that radial inflow, boundary layer depth, and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on Rmax when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes. [ABSTRACT FROM AUTHOR]

Instance segmentation in remote sensing (RS) imagery aims to predict the locations of instances and represent them with pixel-level masks. Thanks to the more accurate pixel-level information for each instance, instance segmentation has enormous potential applications in resource planning, urban surveillance, and military reconnaissance. However, current RS imagery instance segmentation methods mostly follow the fully supervised paradigm, relying on expensive pixel-level labels. Moreover, remote sensing imagery suffers from cluttered backgrounds and significant variations in target scales, making segmentation challenging. To accommodate these limitations, we propose a semantic attention enhancement and structured model-guided multi-scale weakly supervised instance segmentation network (SASM-Net). Building upon the modeling of spatial relationships for weakly supervised instance segmentation, we further design the multi-scale feature extraction module (MSFE module), semantic attention enhancement module (SAE module), and structured model guidance module (SMG module) for SASM-Net to enable a balance between label production costs and visual processing. The MSFE module adopts a hierarchical approach similar to the residual structure to establish equivalent feature scales and to adapt to the significant scale variations of instances in RS imagery. The SAE module is a dual-stream structure with semantic information prediction and attention enhancement streams. It can enhance the network's activation of instances in the images and reduce cluttered backgrounds' interference. The SMG module can assist the SAE module in the training process to construct supervision with edge information, which can implicitly lead the model to a representation with structured inductive bias, reducing the impact of the low sensitivity of the model to edge information caused by the lack of fine-grained pixel-level labeling. Experimental results indicate that the proposed SASM-Net is adaptable to optical and synthetic aperture radar (SAR) RS imagery instance segmentation tasks. It accurately predicts instance masks without relying on pixel-level labels, surpassing the segmentation accuracy of all weakly supervised methods. It also shows competitiveness when compared to hybrid and fully supervised paradigms. This research provides a low-cost, high-quality solution for the instance segmentation task in optical and SAR RS imagery. [ABSTRACT FROM AUTHOR]

Estimations of deforestation are crucial as increased levels of deforestation induce serious environmental problems. However, it is challenging to perform investigations over extensive areas, such as the Amazon rainforest, due to the vast size of the region and the difficulty of direct human access. Satellite imagery can be used as an effective solution to this problem; combining optical images with synthetic aperture radar (SAR) images enables deforestation monitoring over large areas irrespective of weather conditions. In this study, we propose a learning strategy for multi-modal deforestation estimations on this basis. Images from three different satellites, Sentinel-1, Sentinel-2, and Landsat 8, were utilized to this end. The proposed algorithm overcomes visibility limitations due to a long rainy season of the Amazon by creating a multi-modal dataset using supplementary SAR images, achieving high estimation accuracy. The dataset is composed of satellite data taken on a daily basis with relatively less monthly generated, ground truth masking data, which is called the many-to-one-mask condition. The Normalized Difference Vegetation Index and Normalized Difference Soil Index bands are selected to comprise the datasets. This yields better detection performance and a shorter training time than datasets consisting of RGB or all bands. Multiple deep neural networks are independently trained for each modality and an appropriate fusion method is developed to detect deforestation. The proposed method utilizes the distance similarity of the predicted deforestation rate to filter prediction results. The elements with high degrees of similarity are merged into the final result with average and denoising operations. The performances of five network variants of the U-Net family are compared, with Attention U-Net observed to exhibit the best prediction results. Finally, the proposed method is utilized to estimate the deforestation status of novel queries with high accuracy. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) plays a vital role in change detection (CD) analysis due to the ability to produce remote sensing images throughout the day, regardless of weather conditions. Nowadays, deep learning methods have gained popularity in multitemporal SAR image CD applications. However, false labels generated during the preclassification stage limit the performance of the CD process. This work employs a fast and robust fuzzy c-means clustering to generate the pseudo-label samples and lightweight spatial pyramid pooling attention network (SPPANet) to detect changes in multitemporal SAR images. The spatial pyramid structure in SPPANet applies adaptive pooling layers to provide better contextual information without incurring computational overhead. The log-ratio operator is used to generate the difference image (DI), and the pseudo-label samples are created from DI. The pseudo-label samples are used to create the training and testing patches. Finally, the trained SPPANet is used to classify testing samples into unchanged and changed classes. The SPPANet achieves an accuracy of 98.70%, 99.06%, 96.40%, and 99.10% for Ottawa, San Francisco, Yellow River, and Farmland datasets, respectively. [ABSTRACT FROM AUTHOR]

Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR imaging is a form of coherence imaging, meaning that there is a large amount of speckle noise in each SAR image. This seriously affects the detection of an SAR image ship target when the fuzzy C-means (FCM) clustering method is used, resulting in numerous errors and incomplete detection. It is also associated with a slow detection speed, which easily falls into the local minima. To overcome these issues, a new method based on block thumbnail particle swarm optimization clustering (BTPSOC) was proposed for SAR image ship target detection. The BTPSOC algorithm uses block thumbnails to segment the main pixels, which improves the resistance to noise interference and segmentation accuracy, enhances the ability to process different types of SAR images, and reduces the runtime. When particle swarm optimization (PSO) technology is used to optimize the FCM clustering center, global optimization is achieved, the clustering performance is improved, the risk of falling into the local minima is overcome, and the stability is improved. The SAR images from two datasets containing ship targets were used in verification experiments. The experimental results show that the BTPSOC algorithm can effectively detect the ship target in SAR images and that it maintains good integrity with regard to the detailed information from the target region. At the same time, experiments comparing the deep convolution neural network (CNN) and constant false alarm rate (CFAR) were conducted. [ABSTRACT FROM AUTHOR]

Up‐to‐date topography data sets are essential for forecasting volcanic hazards and monitoring deformation. Digital elevation models are used to quantify eruption rates, used in flow modeling programs, and are necessary to accurately process interferometric synthetic aperture radar data for surface deformation. We can track topographic change at volcanoes through fieldwork, airborne instruments, and satellite data, with the last providing the greatest potential for global coverage. Despite this global coverage, we do not know the characteristics of topographic change at volcanoes over a given time interval. We define the specific acquisition needs for topography data using topographic change detected from recent eruptions. We review existing literature and compile a data set of eruptive products (121 lava flows, 99 domes and 163 pyroclastic density currents (PDCs)) from eruptions between 1980 and 2019. We find that different sensing capabilities are required for different use cases. A vertical accuracy of 1 m would detect 92% of all eruptive products including 100% of lava domes and lava flows, but only 78% of PDCs. A horizontal resolution of 13 × 13 m pixels is the minimum necessary to detect 90% of all eruptive products. Explosive eruptions (with PDC products) typically lasted less than 1 day and would need a temporal resolution of 1 day while a longer repeat interval is acceptable at effusive eruptions (lava domes and flows), which could last weeks to years. We find a lack of consistent data acquisition, with 45% of the 383 eruptive products reported not having published spatial dimensions. Plain Language Summary: Surface features in volcanic environments can change frequently, especially during an eruption. Volcanoes can create eruptive products such as lava and ash that change the surface features in the area. Accurate mapping of these surface features is important for tracking the progress of an eruption and for forecasting how the landscape will impact future volcanic hazards, such as lava flows or mudflows. We can track the changes of surface features through fieldwork, airborne instruments such as drones, or with various types of satellite data. We do not know how frequently we need to collect these data at volcanoes or with what spatial resolution. This work fills this knowledge gap through a literature review of the dimensions of products (lava flows, lava domes, and PDCs) erupted between 1980 and 2019. We suggest spatial and temporal resolutions to collect data on surface feature change at volcanoes. A pixel size of 13 m would measure 90% of products of volcanic eruptions and a vertical precision of 1 m would measure 92% of them. A temporal resolution of 1 day or less is needed for explosive eruptions, while a longer repeat interval is acceptable at eruptions with long‐lived lava flows. Key Points: Up‐to‐date topographic change data sets aid in forecasting eruption duration and eruptive hazardsWe suggest spatial and temporal resolutions to capture topographic change at volcanoes based on a review of past eruptive productsCurrent optical and commercial synthetic aperture radar satellites have the resolutions necessary to capture 90% of eruptive products studied [ABSTRACT FROM AUTHOR]

The Arctic is experiencing the greatest increase in air temperature on Earth. This significant climatic change is leading to a significant positive trend of increasing wave heights and greater coastal erosion. This in turn effects local economies and ecosystems. Increasing wave energy is one of the main drivers of this alarming trend. However, the data on spatial and temporal patterns of wave heights in the Arctic are either coarse, interpolated or limited to point measurements. The aim of this study is to overcome this shortcoming by using remote sensing data. In this study, the Synthetic Aperture Radar (SAR) satellite TerraSAR-X (TS-X) and TanDEM-X (TD-X) imagery are used to obtain sea state information with a high spatial resolution in Arctic nearshore waters in the Canadian Beaufort Sea. From the entire archive of the TS-X/TD-X StripMap mode with coverage around 30 km × 50 km acquired between 2009 and 2020 around Herschel Island, Qikiqtaruk (HIQ), all the ice-free scenes were processed. The resulting dataset of 175 collocated scenes was used to map the significant wave height ( H s ) and to link spatial and temporal patterns to local coastal processes. Sea state parameters are estimated in raster format with a 600 m step using the empirical algorithm CWAVE_EX. The statistics of the H s were aggregated according to spatial variability, seasonality and wind conditions. The results show that the spatial wave climate is clearly related to the dominant wind regime and seasonality. For instance, the aggregation of all the scenes recorded in July between 2009 and 2020 results in an average of 0.82 m H s , while in October the average H s is almost 0.40 m higher. The analysis by wind direction shows that fetch length and wind speed are likely the most important variables influencing the spatial variability. A larger fetch under NW conditions results in a mean wave height of 0.92 m, while waves generated under ESE conditions are lower at 0.81 m on average. [ABSTRACT FROM AUTHOR]

This study focuses on analyzing and monitoring urban subsidence, particularly in the city of Palermo, Italy. Land subsidence, induced by natural and human factors, poses threats to infrastructure and urban safety. Remote sensing (RS), specifically synthetic-aperture radar interferometry (In-SAR), is employed due to its ability to detect ground displacements over large areas with great precision. The persistent scatterer InSAR (PS-InSAR) technique is utilized to identify stable targets and track millimeter-level surface deformations. This research spans from October 2014 to October 2021, using Sentinel-1 satellite data to capture ground deformation from various angles. The findings are integrated into an accessible web app (ArcGIS) for local authorities that could be used aiding in urban planning and enhancing safety measures. This study's results offer updated deformation maps, serving as an operational tool to support decision-making and community resilience, emphasizing risk awareness and responsible practices. This study highlights that the exponential expansion of urban areas, which does not take into account historical information, can gravely jeopardize both the integrity of urban infrastructure and the well-being of its inhabitants. In this context, remote sensing technologies emerge as an invaluable ally, used in monitoring and safeguarding the urban landscape. [ABSTRACT FROM AUTHOR]

In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing applications due to its ability to obtain full-polarization information. Compared to the quad-pol SAR, the dual-pol SAR mode has a wider observation swath and is more common in most SAR systems. The goal of reconstructing quad-pol SAR data from the dual-pol SAR mode is to learn the contextual information of dual-pol SAR images and the relationships among polarimetric channels. This work is dedicated to addressing this issue, and a multiscale feature aggregation network has been established to achieve the reconstruction task. Firstly, multiscale spatial and polarimetric features are extracted from the dual-pol SAR images using the pretrained VGG16 network. Then, a group-attention module (GAM) is designed to progressively fuse the multiscale features extracted by different layers. The fused feature maps are interpolated and aggregated with dual-pol SAR images to form a compact feature representation, which integrates the high- and low-level information of the network. Finally, a three-layer convolutional neural network (CNN) with a 1 × 1 convolutional kernel is employed to establish the mapping relationship between the feature representation and polarimetric covariance matrices. To evaluate the quad-pol SAR data reconstruction performance, both polarimetric target decomposition and terrain classification are adopted. Experimental studies are conducted on the ALOS/PALSAR and UAVSAR datasets. The qualitative and quantitative experimental results demonstrate the superiority of the proposed method. The reconstructed quad-pol SAR data can better sense buildings' double-bounce scattering changes before and after a disaster. Furthermore, the reconstructed quad-pol SAR data of the proposed method achieve a 97.08% classification accuracy, which is 1.25% higher than that of dual-pol SAR data. [ABSTRACT FROM AUTHOR]

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing. [ABSTRACT FROM AUTHOR]

Synthetic aperture sonar (sas) systems are designed to observe stationary scatterers located near the sediment interface. Less commonly, a sas system may be used to observe scattering features located above the sonar in the water column. The Undersea Remote Sensing (USRS) project, sponsored by the Office of Naval Research, was a collaborative Directed Research Initiative (DRI) focused on studying dynamic estuarine water column features. During the USRS DRI, researchers from multiple institutions gathered to observe tidal features at various estuaries along the coast of the United States using both in situ and remote sensing techniques, including sas. The first studied estuary was the mouth of the Connecticut River (CTR). Data captured by a sas system deployed during a tidal event were post-processed to create three-dimensional observations of the structure of the leading edge of the CTR's ebb plume front. From these observations, lobed structures similar in scale to previously reported instabilities are revealed, with the present observations providing additional insight regarding the structure of the bubble distribution behind the front. Velocity estimates of plume features were also determined from sas data and shown to compare favorably with concurrent marine radar estimates. [ABSTRACT FROM AUTHOR]

Since the 1960s, meteorological satellites have been able to monitor tropical cyclones and typhoons. Their images have been acquired by passive remote sensing instruments that operate in the visible and infrared bands, where they only display the cloud-top structure of tropical cyclones and make it a challenge to study the air–sea interaction near the sea surface. On the other hand, active remote sensors, such as spaceborne microwave scatterometers and synthetic aperture radars (SARs), can "see" through clouds and facilitate observations of the air–sea interaction processes. However, SAR acquires images and provides the wind field at a much higher resolution, where the eye of a tropical cyclone at surface level can be identified. The backscattered signals received by the SAR can be processed into a high-resolution image and calibrated to represent the normalized radar cross section (NRCS) of the sea surface. In this study, 33 RADARSAT-2 and 102 Sentinel-1 SAR images of Atlantic and Indian Ocean tropical cyclones and Pacific typhoons from 2016 to 2021, which display eye structure, have been statistically analyzed with ancillary tropical cyclone intensity information. To measure the size of the eye, a 34-kt (∼17 m s−1) contour is defined around it and the amount and size of pixels within the eye is utilized to provide its area in square kilometers. Additionally, an azimuthal wavenumber for each shape of the eye was assigned. Results showed that eye areas increase with decreasing wind speed and increasing wavenumber and demonstrate that SAR-derived data are useful for studying tropical cyclones at the air–sea interface and provide results of these behaviors closely to data derived from best track archives. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art performances. However, DNN-based visual recognition systems on UAVs show serious vulnerability to adversarial camouflage patterns on targets and well-designed imperceptible perturbations in real-time images, which poses a threat to safety-related applications. Considering a scenario in which a UAV is suffering from adversarial attack, in this paper, we investigate and construct two ensemble approaches with CNN and transformer for both proactive (i.e., generate robust models) and reactive (i.e., adversarial detection) adversarial defense. They are expected to be secure under attack and adapt to the resource-limited environment on UAVs. Specifically, the probability distributions of output layers from base DNN models in the ensemble are combined in the proactive defense, which mainly exploits the weak adversarial transferability between the CNN and transformer. For the reactive defense, we integrate the scoring functions of several adversarial detectors with the hidden features and average the output confidence scores from ResNets and ViTs as a second integration. To verify their effectiveness in the recognition task of remote sensing images, we conduct experiments on both optical and synthetic aperture radar (SAR) datasets. We find that the ensemble model in proactive defense performs as well as three popular counterparts, and both of the ensemble approaches can achieve much more satisfactory results than a single base model/detector, which effectively alleviates adversarial vulnerability without extra re-training. In addition, we establish a one-stop platform for conveniently evaluating adversarial robustness and performing defense on recognition models called AREP-RSIs, which is beneficial for the future research of the remote sensing field. [ABSTRACT FROM AUTHOR]

Worldwide, transport infrastructure is increasingly vulnerable to aging-induced deterioration and climate-related hazards. Often, inspection and maintenance costs far exceed the available resources, and numerous assets lack any rigorous structural evaluation. Space-borne synthetic aperture radar interferometry (InSAR) is a powerful remote sensing technology that can provide cheaper deformation measurements for bridges and other transport infrastructure with short revisit times, while scaling from the local to the global scale. As recent studies have shown InSAR accuracy to be comparable to that of traditional monitoring instruments, InSAR could offer a cost-effective tool for long-term, near-continuous deformation monitoring, with the possibility of supporting inspection planning and maintenance prioritisation while maximising functionality and increasing the resilience of infrastructure networks. However, despite the high potential of InSAR for structural monitoring, some important limitations need to be considered when applying it in practice. In this paper, the challenges of using InSAR for the purpose of structural monitoring are identified and discussed, with specific focus on bridges and transport networks. Examples are presented to illustrate the current practical limitations of InSAR, and possible solutions and promising research directions are identified. The aim of the paper is to motivate future action in this area and highlight the InSAR advances needed to overcome current challenges. [ABSTRACT FROM AUTHOR]

Soil total nitrogen (STN) is a crucial component of the ecosystem's nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R2 of 0.627, RMSE of 0.127 g·kg−1, and MAE of 0.092 g·kg−1. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R2 improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling. [ABSTRACT FROM AUTHOR]

Optical remote sensing images are often contaminated by clouds and shadows, resulting in missing data, which greatly hinders consistent Earth observation missions. Cloud and shadow removal is one of the most important tasks in optical remote sensing image processing. Due to the characteristics of active imaging that enable synthetic aperture radar (SAR) to penetrate cloud cover and other climatic conditions, SAR data are extensively utilized to guide optical remote sensing image cloud and shadow removal. Nevertheless, SAR data are highly corrupted by speckle noise, which generates artifact pollution to spectral features extracted from optical images and makes SAR-optical fusion ill-posed to generate cloud and shadow removal results while retaining high spectral fidelity and reasonable spatial structures. To overcome the aforementioned drawbacks, this paper presents a novel hierarchical spectral and structure-preserving fusion network (HS2P), which can recover cloud and shadow regions in optical remote sensing imagery based on the hierarchical fusion of optical and SAR remote sensing imagery. In HS2P, we present a deep hierarchical architecture with stacked residual groups (ResGroups), which progressively constrains the reconstruction. To pursue the adaptive selection of more informative features for fusion and reduce attention to the features with artifacts brought by clouds and shadows in optical data or speckle noise in SAR data, residual blocks with a channel attention mechanism (RBCA) are recommended. Additionally, a novel collaborative optimization loss function is proposed to preserve spectral features while enhancing structural details. Extensive experiments on the publicly open dataset (i.e. , SEN12MS-CR) demonstrate that the proposed method can robustly recover diverse ground information in optical remote sensing imagery with various cloud types. Compared with the state-of-the-art cloud and shadow removal methods, our HS2P achieves significant improvements in terms of quantitative and qualitative results. The source code is publicly available at https://github.com/weifanyi515/HS2P. • Hierarchical special and structure-preserving fusion network is proposed. • Residual block with channel attention is designed to exploit informative features. • A collaborative optimization loss aims to keep both spectral and structure details. • The promising cloud and shadow removal performance is achieved. [ABSTRACT FROM AUTHOR]

The interpretation of optical and synthetic aperture radar (SAR) images in remote sensing is general for many tasks, such as environmental monitoring, marine management, and resource planning. Instance segmentation of optical and SAR images, which can simultaneously provide instance-level localization and pixel-level classification of objects of interest, is a crucial and challenging task in image interpretation. Considering that most current methods for instance segmentation of optical and SAR images rely on expensive pixel-level annotation, we develop a weakly supervised instance segmentation (WSIS) method to balance the visual processing requirements with the annotation cost. First, we decompose the prior knowledge of the mask-aware task in WSIS into three meta-knowledge components: fundamental knowledge, apparent knowledge, and detailed knowledge inspired by human visual perception habits of "whole to part" and "coarse to detailed." Then, a meta-knowledge-guided weakly supervised instance segmentation network (MGWI-Net) is proposed. In this network, the weakly supervised mask (WSM) head can instantiate both fundamental knowledge and apparent knowledge to perform mask awareness without any annotations at the pixel level. The network also includes a mask information awareness assist (MIAA) head, which can implicitly guide the network to learn detailed information about edges through the boundary-sensitive feature of the fully connected conditional random field (CRF), facilitating the instantiation of detailed knowledge. The experimental results show that the MGWI-Net can efficiently generate instance masks for optical and SAR images and achieve the approximate instance segmentation results of the fully supervised method with about one-eighth of the annotation production time. The model parameters and processing speed of our network are also competitive. This study can provide inexpensive and convenient technical support for applying and promoting instance segmentation methods for optical and SAR images. [ABSTRACT FROM AUTHOR]

Due to the aircraft targets' uncooperative character, acquiring the complete scattering characteristics of enemy aircraft is challenging. An effective reconstruction method of target scattering centres based on characteristics analysis of inverse synthetic aperture radar (ISAR) images is proposed in this letter. First, a positive definite manifold characterizing ISAR images of different attitudes is constructed. Then, Kullback–Leibler divergence is used as a measure of the variability between ISAR images. Finally, an ISAR image interpolation method based on the characteristics analysis of ISAR images is proposed to obtain the scattering characteristics with more attitudes. Furthermore, simulation results confirm that the proposed method is effective. [ABSTRACT FROM AUTHOR]

In an era of rapid technological improvements, state-of-the-art methodologies and tools dedicated to protecting and promoting our cultural heritage should be developed and extensively employed in the contemporary built environment and lifestyle. At the same time, sustainability principles underline the importance of the continuous use of historic or vernacular buildings as part of the building stock of our society. Adopting a holistic, integrated, multi-disciplinary strategy can link technological innovation with the conservation and restoration of heritage buildings. This paper presents the ongoing research and results of the application of Machine Learning methods for the remote monitoring of the built environment of the historic cluster in Cypriot cities. This study is part of an integrated, multi-scale, and multi-disciplinary study of heritage buildings, with the end goal of creating an online HBIM platform for urban monitoring. [ABSTRACT FROM AUTHOR]

SAR-ATR (synthetic aperture radar-automatic target recognition) is a hot topic in remote sensing. This work suggests a few-shot target recognition approach (FTL) based on the concept of transfer learning to accomplish accurate target recognition of SAR images in a few-shot scenario since the classic SAR ATR method has significant data reliance. At the same time, the strategy introduces a model distillation method to improve the model's performance further. This method is composed of three parts. First, the data engine, which uses the style conversion model and optical image data to generate image data similar to SAR style and realize cross-domain conversion, can effectively solve the problem of insufficient training data of the SAR image classification model. Second is model training, which uses SAR image data sets to pre-train the model. Here, we introduce the deep Brownian distance covariance (Deep BDC) pooling layer to optimize the image feature representation so that the model can learn the image representation by measuring the difference between the joint feature function of the embedded feature and the edge product. Third, model fine-tuning, which freezes the model structure, except the classifier, and fine-tunes it by using a small amount of novel data. The knowledge distillation approach is also introduced simultaneously to train the model repeatedly, sharpen the knowledge, and enhance model performance. According to experimental results on the MSTAR benchmark dataset, the proposed method is demonstrably better than the SOTA method in the few-shot SAR ATR issue. The recognition accuracy is about 80% in the case of 10-way 10-shot. [ABSTRACT FROM AUTHOR]

Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures. [ABSTRACT FROM AUTHOR]

Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized synthetic aperture radar data from different periods (Gaofen-3) to distinguish mangrove species in the Fucheng town of Leizhou, Guangdong Province. The Gaofen-1 data were fused with Sentinel-2 and Landsat-9 satellite data, respectively. The new data after fusion had both high spatial and spectral resolution. The backscattering coefficient and polarization decomposition parameters of the fully polarized SAR data which could characterize the canopy structure of mangroves were extracted. Ten different feature combinations were designed by combining the two types of data. The extremely randomized trees algorithm (ERT) was used to classify the species, and the optimal feature subset was selected by the feature selection algorithm on the basis of the ERT, and the importance of the features was sorted. Studies show the following: (1) When controlling a single variable, the higher the spatial resolution of the multi-spectral data, the higher the interspecific classification accuracy. (2) The coupled Sentinel-2 and Landsat-9 data with a 2 m resolution will have higher classification accuracy than a single data source. (3) The selected feature subset contains all types of features in the optical data and the polarization decomposition features of the SAR data from different periods: multi-spectral band > texture feature > polarization decomposition parameter > vegetation index. Among the optimized feature combinations, the classification accuracy of mangrove species was the highest, the overall classification accuracy was 90.13%, and Kappa was 0.84, indicating that multi-source and SAR data from different periods coupling could improve the discrimination of mangrove species. (4) The ERT classification algorithm is suitable for the study of mangrove species classification, and the classification accuracy of extremely random trees in this paper is higher than that of random forest (RF), K-nearest neighbor (KNN), and Bayesian (Bayes). The results can provide technical guidance and data support for mangrove species monitoring based on multi-source satellite data. [ABSTRACT FROM AUTHOR]

In recent years, semantic segmentation with pixel-level classification has become one of the types of research focus in the field of polarimetric synthetic aperture radar (PolSAR) image interpretation. Fully convolutional network (FCN) can achieve end-to-end semantic segmentation, which provides a basic framework for subsequent improved networks. As a classic FCN-based network, U-Net has been applied to semantic segmentation of remote sensing images. Although good segmentation results have been obtained, scalar neurons have made it difficult for the network to obtain multiple properties of entities in the image. The vector neurons used in the capsule network can effectively solve this problem. In this paper, we propose a complex-valued (CV) U-Net with a CV capsule network embedded for semantic segmentation of a PolSAR image. The structure of CV U-Net is lightweight to match the small PolSAR data, and the embedded CV capsule network is designed to extract more abundant features of the PolSAR image than the CV U-Net. Furthermore, CV dynamic routing is proposed to realize the connection between capsules in two adjacent layers. Experiments on two airborne datasets and one Gaofen-3 dataset show that the proposed network is capable of distinguishing different types of land covers with a similar scattering mechanism and extracting complex boundaries between two adjacent land covers. The network achieves better segmentation performance than other state-of-art networks, especially when the training set size is small. [ABSTRACT FROM AUTHOR]

The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors. [ABSTRACT FROM AUTHOR]

Accurate monitoring of the spatiotemporal dynamics of snow and ice is essential for under-standing and predicting the impacts of climate change on Arctic ecosystems and their feedback on global climate. Traditional optical and Synthetic Aperture Radar (SAR) remote sensing still have limitations in the long-time series observation of polar regions. Although several studies have demonstrated the potential of moonlight remote sensing for mapping polar snow/ice covers, systematic evaluation on applying moonlight remote sensing to monitoring spatiotemporal dynamics of polar snow/ice covers, especially during polar night periods is highly demanded. Here we present a systematic assessment in Svalbard, Norway and using data taken from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) Day/Night Band (DNB) sensor to monitor the spatiotemporal dynamics of snow/ice covers during dark Arctic winters when no solar illumination available for months. We successfully revealed the spatiotemporal dynamics of snow/ice covers from 2012 to 2022 during polar night/winter periods, using the VIIRS/DNB time series data and the object-oriented Random Forests (RF) algorithm, achieving the average accuracy and kappa coefficient of 96.27% and 0.93, respectively. Our findings indicate that the polar snow/ice covers show seasonal and inter-seasonal dynamics, thus requiring more frequent observations. Our results confirm and realize the potential of moonlight remote sensing for continuous monitoring of snow/ice in the Arctic region and together with other types of remote sensing data, moonlight remote sensing will be a very useful tool for polar studies and climate change. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar with polarimetric diversity is a powerful tool in remote sensing. Each pixel is described by the scattering matrix corresponding to the emission/reception polarization states (usually horizontal and vertical). The algebraic real representation, a block symmetric matrix form, is introduced to adopt a more comprehensive framework (non-restricted by reciprocity assumptions) in mapping the scattering matrix by the consimilarity equivalence relation. The proposed representation can reveal potentially new information. For example, its eigenvalue decomposition, which is itself a necessary step in obtaining the consimilarity transformation products, may be useful in characterizing the degree of reciprocity/nonreciprocity. As a consequence, it can be employed in testing the reciprocity compliance assumed with monostatic PolSAR data. Full-wave simulated polarimetric data confirm that oriented scatterers can present complex eigenvalues, even with the monostatic geometry. [ABSTRACT FROM AUTHOR]

Diamond and gold mining has been practiced by artisanal miners in the Central African Republic (CAR) for decades. The recent introduction of riverine dredges indicates a transition from artisanal/manual digging and sorting techniques to small-scale mining methods. This study implements a remote sensing analysis of Synthetic Aperture Radar (SAR) data to map gold and diamond dredges operating on the Kadéï (Sangha) river in the CAR. Riverine vessels are identified in Sentinel-1 SAR data between 2015 and 2019, and their activity levels are mapped over time. The number of active dredges identified on the river increased over the five years studied, with the largest increase occurring between 2016 and 2017. Detailing a method for mapping and monitoring riverine diamond and gold dredge mining is an important step in keeping up with evolving technologies and new areas of mineral exploitation and in helping address concerns over resource governance in remote and conflict-prone terrain. The use of SAR technology, with its weather-independence, broad coverage, and available wavelength combinations, allows for higher temporal resolution and improved vessel detection in the monitoring of small-scale mining (SSM) dredges. [ABSTRACT FROM AUTHOR]

In this paper, the problem of restoration of cloud contaminated optical images is studied in the case when we have no information about brightness of such images in the damage region. We propose a new variational approach for exact restoration of optical multi-band images utilising Synthetic Aperture Radar (EOS – Spatial Data Analytics, GIS Software, Satellite Imagery – is a cloud-based platform to derive remote sensing data and analyse satellite imagery for business and science purposes) images of the same regions. We prove existence of solutions, propose an alternating minimisation method for computing them, prove convergence of this method to weak solutions of the original problem and derive optimality conditions. [ABSTRACT FROM AUTHOR]

A massive landslide often causes long-lasting instability dynamics that need to be analyzed in detail for risk management and mitigation. Multiple satellite remote sensing observations, in-situ measurements, and geophysical approaches have been jointly implemented to monitor and interpret the life cycle of landslides and their failure mechanisms from various perspectives. In this work, we propose a framework where satellite optical and synthetic aperture radar (SAR) remote sensing techniques are combined with feature extractions using independent component analysis (ICA) and a mathematical relaxation model to assess the complete four-dimensional (4D) spatiotemporal patterns of post-failure slope evolution. The large, deep-seated Aniangzhai landslide in Southwest China that occurred on 17 June 2020 is comprehensively analyzed and characterized for its post-failure mechanism. Time series of Planet high-resolution optical images are first explored to derive the large horizontal motions for the first six months after the failure. Spatiotemporal dynamics of line-of-sight (LOS) displacement in the landslide body are then derived between November 2020 and February 2022 by combining 40 TerraSAR-X (TSX) High-resolution Spotlight (HS) images and 76 medium-resolution Sentinel-1 (S1) SAR datasets using Multi-temporal InSAR (MTI) method. The InSAR-derived results are subsequently analyzed with ICA to find common deformation components of points between optical and MTI results, indicating the same temporal evolution in the deformation pattern. Finally, the complete 4D deformation field for the whole post-failure period is modeled using a decaying exponential model representing stress relaxation after the failure by integrating multiple remote sensing datasets. Cross-correlation analysis of Planet imagery shows a decaying exponential pattern of post-failure displacements with an approximately 94% reduction in the deformation rate after six months with respect to the co-failure event. MTI analysis suggests a maximum LOS displacement rate of approximately 30 cm/year over the main failure body from November 2020 to February 2022; while the high-resolution TSX datasets show irreplaceable advantages in choosing the number of measurement points in MTI analysis with the number of measurement points being five times larger than those obtained by S1 datasets. The ICA analysis reveals three main types of kinematic patterns in the temporal evolution of post-failure deformation in MTI results, the dominant one being an exponential declining pattern similar to the results from Planet observations. Integrated 4D deformation modeling suggests that the most significant post-failure displacement mainly occurred toward the west, amounting to 28 m during the entire post-failure acquisitions from June 2020 until February 2022. Additionally, maximum displacements of 17 m and 19 m occurred in this period toward the north and downward, respectively. [Display omitted] • Monitor Aniangzhai landslide integrating multi-sensor remote sensing data • Derive 4D post-failure slope instability dynamics • Implement ICA to analyze spatiotemporal characteristics of slope deformation • Propose a relaxation model to predict post-failure deformation [ABSTRACT FROM AUTHOR]

This paper addresses the unsupervised classification problem for multilook polarimetric synthetic aperture radar (PolSAR) images via proposing a new spatially variant G p 0 mixture model based on the extended variational inference (SV G p 0 MM-EVI). Specifically, an SV G p 0 MM is constructed by associating data points with their own mixing coefficient vectors rather than sharing the same vector, which provides the additional flexibility to incorporate the spatial context and further effectively deal with the more heterogeneous areas than G p 0 MM. Then, an extended variational inference (EVI) algorithm is derived to facilitate the assignment of the conjugate prior distributions and accurately estimate the underlying parameters, in which two "help" functions are specially designed to solve the intractable variational lower bound. Meanwhile, dual spatial constraints based on the Bartlett distance and the mean template are also explored on polarimetric matrix and the hyperparameter in the posteriori distribution of mixing coefficients, respectively, thus adequately capturing the local correlation from the polarimetric matrix and the geometry perspectives. Furthermore, the learning algorithm with all the closed-form updates is developed and the cluster number could be automatically determined. Five PolSAR data sets (including two labeled data sets), which are obtained by different sensors, are used to verify the effectiveness of the proposed model. Experimental results illustrate that the proposed SV G p 0 MM-EVI is beneficial to classification task of PolSAR images, and achieves higher accuracy (e.g., 95.65% and 97.41% OA values) compared with some widely-used methods (i.e., H/ α -Wishart, Chernoff–Wishart, G p 0 MM, SVWMM and CK-HDRF methods). [ABSTRACT FROM AUTHOR]

When synthetic aperture radar (SAR) is conducting remote sensing or terrain mapping, its radar beam is inevitably occluded by the variations in the under-test topography. Although back-projection algorithm (BPA) can theoretically directly solve the imaging problems of topography variations that most current SAR imaging algorithms cannot handle, these BPAs only solve the phase focusing of SAR echo signal, and do not consider the mismatch of SAR imaging results caused by topography occlusion. To solve the mis-imaging issue of the occluded area generated by BPA under the case of topography variation, a topography-based BPA (Topo-BPA) is proposed in this paper. Firstly, a new beam occlusion judgment algorithm based on spherical wave assumption is proposed, and its core is depression angle interpolation and depression angle updating. Then, the proposed Topo-BPA embeds the proposed beam occlusion judgment algorithm before the classical BPA, which not only did not reduce the focus depth of BPA, but improved the imaging accuracy of classical BPA. Finally, numerical experiments have demonstrated the superiority of the Topo-BPA's performance in comparison with classical BPA. [ABSTRACT FROM AUTHOR]

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l'Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data. [ABSTRACT FROM AUTHOR]

Automatic and high-precision extraction of buildings from remote sensing images has a wide range of application and importance. Optical and synthetic aperture radar (SAR) images are typical types of multimodal remote sensing data with different imaging methods. To bridge the huge gap between them and achieve high-precision joint semantic segmentation, this study proposes a progressive fusion learning framework. The framework explicitly extracts the shared features (that is, modal invariants) of multimodal images as the information medium and realizes information fusion through multistage learning. Based on this framework, we design a network called the multistage multimodal fusion network (MMFNet), which uses phase as a modal invariant to joint optical and SAR images to achieve high-precision building extraction. We conducted experiments with the Multi-Sensor All-Weather Mapping aerial dataset and the WHU-OPT-SAR_WuHan satellite dataset. This study shows MMFNet has a significant extraction effect and yields more optimized extraction of the edge details of buildings, which is improved by 0.2% to 9.5% compared to other multimodal joint segmentation methods. [ABSTRACT FROM AUTHOR]

This paper examines a feature-level fusion framework for detecting and mapping land degradation (LD) and enabling sustainable land management (SLM) in semi-arid areas using optical sensors and Synthetic Aperture Radar (SAR) satellite data. The objectives of this review were to (i) determine the trends and geographical location of land degradation mapping publications, (ii) to identify and report current challenges pertaining to mapping LD using multiscale remote sensing data, (iii) to recommend a way forward for monitoring LD using multiscale remote sensing data. The study reviewed 78 peer-reviewed research articles published over the past 24 years (1998–2022). Image fusion has the potential to be more useful in various remote sensing applications than individual sensor image data, making it more informative and valuable in the interpretation process. In addition, this review discusses the importance of SAR and optical image fusion, pixel-level techniques, applications, and major classes of quality metrics for objectively assessing fusion performance. The literature review alluded that the SAR and optical image fusion in the detection and mapping of land degradation and enabling sustainable land management has not been fully explored. Advanced techniques such as the fusion of SAR and optical satellite imageries need to be incorporated for the detection and mapping of LD, as well as the promotion of SLM in halting LD in South African drylands and around the world. We conclude that there is scope for further research on the fusion of SAR and optical images, as new micro-wave and optical sensors with higher resolution are introduced on a regular basis. The results of this review contribute to a better understanding of the applications of SAR and optical image fusion in future research in the severely degraded drylands of southern Africa. The fusion of SAR and optical data still remains an open challenge. The future of different remote sensing applications lies in this kind of fusion. Land degradation is one of the greatest challenges amongst the environmental problems in South Africa, causing a reduction in the capacity of the land to perform ecosystem functions and services that support society and development. Yet, in South Africa, there are no studies that have widely investigated the potential for a fusion of SAR and optical data to detect and map land degradation and SLM practices. This paper established a baseline for understanding the application of a fusion of SAR and optical data as rapid tools for mapping, monitoring, and evaluating LD, as well as the impacts of SLM practices in South Africa's degraded drylands. [ABSTRACT FROM AUTHOR]

Generation of digital elevation models (DEM) over densely vegetated humid tropics using Sentinel-1 data and Interferometry synthetic aperture radar (InSAR) technique is revised based on recommended optimum perpendicular baselines, and accuracy assessment based on coherence cells is introduced. Six pairs of Sentinel-1 data within the optimum perpendicular baseline of 150( 400 m and different temporal baselines acquired between 2015 and 2021 were processed using Sentinel Application Platform. Results revealed: that due to the extreme difficulty of achieving good coherence, interferograms are characterized by noise, generated DEMs are dominated by artefacts, higher elevation accuracy can be achieved within coherence cells, and within the recommended optimum perpendicular baseline range, elevation accuracy within coherence cells is mainly determined by the perpendicular baseline compared to temporal baseline. Therefore, this study can provide practical guide for InSAR generated DEMs evaluation in densely vegetated environments, and remote sensing method for regional-scale measurement and mapping of spots heights. [ABSTRACT FROM AUTHOR]

The development of deep learning and remote sensing makes it possible to obtain information about oceanic internal waves from massive data. Oceanic internal waves play an important role in ocean activities. To obtain the light and dark stripes from synthetic aperture radar (SAR) images, a stripe segmentation algorithm for oceanic internal waves is proposed based on Mask RCNN. The research results show that the proposed method can not only identify whether there are oceanic internal waves, but also obtain the respective locations of the light and dark stripes in the images. The width and the direction angle of each light and dark stripe can be obtained efficiently by a separation and matching method with the sector region (SMMSR). Moreover, based on the identified relative locations of the light and dark stripes, it is possible to further determine the time when the oceanic internal waves undergo polarity conversion. [ABSTRACT FROM AUTHOR]

The high spatial and temporal resolution of water body data offers valuable guidance for disaster monitoring and assessment. These data can be employed to quickly identify water bodies, especially small water bodies, and to accurately locate affected areas, which is significant for protecting people's lives and property. However, the application of optical remote sensing is often limited by clouds and fog during actual floods. In this paper, water extraction methods of the multisource data fusion model (MDFM) and superpixel water extraction model (SWEM) are proposed, in which the MDFM fuses optical and synthetic aperture radar (SAR) images, and all-weather water extraction is achieved by using spectral information of optical images, texture information and the good penetration performance of SAR images. The SWEM further improves the accuracy of the water boundary with superpixel decomposition for extracted water boundaries using the fully constrained least squares (FCLS) method. The results show that the correlation coefficient (r) and area accuracy (Parea) of the MDFM and SWEM are improved by 2.22% and 9.20% (without clouds), respectively, and 3.61% and 18.99% (with clouds), respectively, compared with the MDFM, and 41.54% and 85.09% (without clouds), respectively, and 32.31% and 84.31% (with clouds), respectively, compared with the global surface water product of the European Commission Joint Research Centre's Global Surface Water Explorer (JRC-GSWE). The MDFM and SWEM can extract water bodies with all weather and superpixel and improve the temporal and spatial resolution of water extraction, which has obvious advantages. [ABSTRACT FROM AUTHOR]

Currently, under supervised learning, a model pre-trained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated knowledge transfer learning. Unfortunately, due to different categories of imaging data and stiff challenges of data annotation, there is not a large enough and uniform remote sensing dataset to support large-scale pre-training in the remote sensing domain (RSD). Moreover, pre-training models on large-scale nature scene datasets by supervised learning and then directly fine-tuning on diverse downstream tasks seems to be a crude method, which is easily affected by inevitable incorrect labeling, severe domain gaps and task-aware discrepancies. Thus, in this paper, considering the self-supervised pre-training and powerful vision transformer (ViT) architecture, a concise and effective knowledge transfer learning strategy called ConSecutive Pre-Training (CSPT) is proposed based on the idea of not stopping pre-training in natural language processing (NLP), which can gradually bridge the domain gap and transfer large-scale data knowledge to any specific domain (e.g., from nature scene domain to RSD) In addition, the proposed CSPT also can release the huge potential of unlabeled data for task-aware model training. Finally, extensive experiments were carried out on twelve remote sensing datasets involving three types of downstream tasks (e.g., scene classification, object detection and land cover classification) and two types of imaging data (e.g., optical and synthetic aperture radar (SAR)). The results show that by utilizing the proposed CSPT for task-aware model training, almost all downstream tasks in the RSD can outperform the previous knowledge transfer learning strategies based on model pre-training without any expensive manually labeling and even surpass the state-of-the-art (SOTA) performance without any careful network architecture designing. [ABSTRACT FROM AUTHOR]

Coastal zone areas are important parts of the environment, very often subject to constant change. Makar, in [[6]], proposed a simplified method for determining the speed of sound in water based on temperature measurements and salinity prediction for shallow-water bathymetry. Surface modelling was carried out using data acquired by modern remote sensing sensors, such as a single-beam echo sounder and an RGB camera mounted on an unmanned aerial platform. Increasingly detailed and diverse remote sensing data of the aquatic environment and adjacent areas enable comprehensive studies, often related to the increasing resolution, completeness, availability, or the development of the survey instruments themselves. [Extracted from the article]

Polarimetric synthetic aperture radar (PolSAR) systems are an important remote sensing tool. Such systems can provide high spacial resolution images, but they are contaminated by an interference pattern called multidimensional speckle. This fact requires that PolSAR images receive specialised treatment; particularly, tailored models which are close to PolSAR physical formation are sought. In this paper, we propose two new matrix models which arise from applying the stochastic summation approach to PolSAR, called compound truncated Poisson complex Wishart (CTPCW) and compound geometric complex Wishart (CGCW) distributions. These models offer the unique ability to express multimodal data. Some of their mathematical properties are derived and discussed—characteristic function and Mellin-kind log-cumulants (MLCs). Moreover, maximum likelihood (ML) estimation procedures via expectation maximisation algorithm for CTPCW and CGCW parameters are furnished as well as MLC-based goodness-of-fit graphical tools. Monte Carlo experiment results indicate ML estimates perform at what is asymptotically expected (small bias and mean square error) even for small sample sizes. Finally, our proposals are employed to describe actual PolSAR images, presenting evidence that they can outperform other well-known distributions, such as W m C , G m 0 , and K m . [ABSTRACT FROM AUTHOR]