Search

Drone-based synthetic aperture radar (SAR) analysis offers a simple and efficient technique for acquiring observational data from various research areas. However, fundamental research on observation techniques, including the investigation of drone flight parameters, remains insufficient. This study focuses on improving the resolution of intensity images of a single-look complex (SLC) via drone-based SAR observations. By conducting experiments at different flight speeds and analysing the obtained SLC images, we demonstrated that higher drone speeds resulted in enhanced image resolution under the conditions investigated at flight speeds of ≤ 5 m s−1. A comparison with satellite SAR images highlighted the superior performance of drone-based SAR in capturing topographic features. The analysis also suggested that factors including trajectory deviation and Doppler rate affected the resolution and azimuth position. The simulation results supported the experimental findings on the relationship between drone speed and resolution. Our results emphasize the potential of drone-based SAR observations for monitoring slope failures and suggest the importance of the drone flight parameter selection. In the future, drone-based SAR is expected to produce intensity images of slopes and locate slope deformations associated with blasting for mining and earthquakes. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) images have been widely used for surface water identification due to their all-weather capabilities. However, the presence of inherent speckle noise in SAR data poses a challenge for accurate water identification. Additionally, annotating high-quality water body samples requires significant human labour, which can be costly and time-consuming. Aiming at the above problems, a noise-robust automatic water identification architecture without artificial labels is proposed. First, a two-stage automatic sample collection method that utilizes k-means++ clustering and morphological concepts is designed. Then, a weakly supervised noise-resistant SAR water body segmentation method NRM-ACUNet has been developed based on U-Net combined with LNR-Dice loss function and Conditionally Parameterized Convolutions (CondConv) to minimize the impact of sample noises. Experimental results show that the morphological processing can improve water body sample quality compared to k-means++, and compared with U-Net, NRM-ACUNet performs superior with noise-containing pseudo-samples, achieving 96.8% F1 accuracy and 52.06% accuracy improvement. [ABSTRACT FROM AUTHOR]

In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m. [ABSTRACT FROM AUTHOR]

Matching optical and synthetic aperture radar (SAR) images is often challenged by intricate geometric distortion and nonlinear radiation differences, leading to insufficient and unevenly distributed corresponding points. To tackle this issue, we propose a lightweight deep convolutional network with inverted residuals for optical and SAR image matching. Initially, a fully convolutional neural network (FCNN) is designed to extract high-level and semantic features, robustly capturing universal characteristics between optical and SAR images, adept at handling geometric distortion and nonlinear radiation changes. Notably, we integrate a lightweight architecture with inverted residuals into FCNN to adeptly extract local and global contextual information, facilitating feature reuse and minimizing the loss of crucial features. Additionally, a vector-refined module is deployed to refine dense features, filtering out redundant information. Subsequently, a coarse-to-fine strategy is employed to further eliminate gross errors or incorrect matches. Finally, we evaluate the performance of the proposed network in optical and SAR image matching against manually-designed methods and state-of-the-art deep learning techniques. Experimental results demonstrate that our network significantly surpasses existing methods in terms of the number of correct matches and matching accuracy. Specifically, our proposed network achieves at least a 2.8 times increase in correct matches and an 18% improvement in matching accuracy compared to existing methods. [ABSTRACT FROM AUTHOR]

Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA's Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests. [ABSTRACT FROM AUTHOR]

Classification with a few samples of training set has been a long-standing issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. In fact, one of the most important challenges of the PolSAR image classification task is the number of labelled samples. In essence, training network by utilizing just few numbers of training samples cannot be led to the reliable result since the neural network is so sensitive to the number of training samples. In addition, in the real PolSAR image classification task, the huge number of training samples is not accessible. On the other hand, classification performance using just small number of labelled samples is not an accurate result. So, in this paper, aiming at the small number of training samples of the PolSAR image classification task, a novel ensemble self-supervised feature-learning (ESSFL) model is designed. The designed ESSFL can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, to utilize the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet-B0 is presented and utilized as the main section of the deep learning (DL) model to extract DL features of PolSAR data. Then, using an optimization function that constrains the cross-correlation matrix of various distortions of each sample to the identity matrix, the designed deep learning model can obtain the effective features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. Moreover, two ensemble learning (EL) models, feature-level and view-level ensemble, are proposed to increase the feature extraction capability and classification result by jointly using spatial features at different scales and polarimetric information at different bands. Finally, the stack of the obtained features and the main polarimetric information of PolSAR data can be input into classifier for the classification of PolSAR data. In this paper we use two different classifiers, the first one is the Random Forest (RF) classifier and the second one is the Support Vector Machine (SVM) classifier. The advantages of SVM include that it can be used to avoid the difficulties of using linear functions in the high-dimensional feature space, and the optimization problem is transformed into dual convex quadratic programs that are so usable for an actual image classification task. In addition, among all the available classification methods, random forests provide one of the highest accuracies. The random forest technique can also handle big data with numerous variables running into thousands. In fact, it can automatically balance data sets when a class is more infrequent than other classes in the data that is so important for a real PolSAR image classification task. Experimental results on three well-known PolSAR data sets illustrate that the designed ESSFL can extract more discriminant features using the designed deep learning model. In the end, the experiments prove that the designed ESSFL has a significant classification performance compared with different deep learning models in the case of small number of training samples, and also it can achieve a better result in the case of large number of training samples by comparison with the most of the deep learning models. [ABSTRACT FROM AUTHOR]

Based on particle swarm optimization-back propagation (PSO-BP) neural network, a novel inverse synthetic aperture radar (ISAR) image quality assessment (IQA) method is proposed to address the challenge of selecting high-quality ISAR images from a large number of images. This method incorporates six assessment metrics: energy gradient (EG), effective area ratio of image (EARI), image contrast (IC), image entropy (IE), equivalent number of looks (ENL), and signal-to-noise ratio (SNR) to evaluate the quality of ISAR images. The PSO-BP neural network is employed to fit the relationship between these metrics and ISAR image quality. Experimental results show that the new method is effective in ISAR-IQA under different imaging conditions and various attitudes, and closely matches the subjective evaluation of experts. [ABSTRACT FROM AUTHOR]

This study focuses on building a paddy rice map for the Red River Delta region (Vietnam) during the spring crop of 2019. Experiments were conducted with traditional machine learning models (XGBoost, LightGBM) and deep learning models (U-Net, Linknet, DeeplabV3+) based on satellite data from Sentinel 1, Sentinel 2, and topographic maps. The experimental models all gave good evaluation results. The rice maps have good agreements with statistics from the government. The results highlighted that the combination of synthetic aperture radar and optical data with machine learning, deep learning models is an effective approach for short-term high-resolution paddy rice mapping. [ABSTRACT FROM AUTHOR]

With the advancement of spaceborne synthetic aperture radar (SAR) resolution, the conventional stop-and-go approximation-based motion model is no longer suitable for high-resolution imaging algorithms. This paper conducts a comprehensive analysis of the approximation errors resulting from the stop-and-go approximation and proposes corresponding compensation methods. Firstly, an accurate echo model based on continuous platform motion is derived from the SAR geometry configuration. The impact of these errors on range and azimuth imaging is analysed from a spectral perspective, and the applicable boundary conditions are examined. Subsequently, compensation methods are proposed to eliminate the introduced approximation errors, addressing both"fast time" and"slow time" effects, thus enabling high-resolution imaging through improvements in traditional imaging algorithms. Experimental simulations validate the effectiveness of the error analysis and compensation methods, using X-band low Earth orbit SAR as an illustrative example. The proposed methods are not only compatible with existing mature imaging algorithms, but the analytical approach and compensation methods are also applicable to SAR systems operating on different carrier frequencies, orbits, and platforms. [ABSTRACT FROM AUTHOR]

Geosynthetic reinforcement has proven effective in mitigating reflective cracking in new pavement overlays. This study aimed to compare the ability of five different geosynthetic reinforcement products to alleviate cracking in overlays. Each product was sandwiched within a two-layered asphalt concrete (AC) beam specimen and subjected to a four-point notched beam fatigue test. The study monitored crack propagation, understood crack propagation mechanisms, and quantified crack lengths in different geosynthetic-reinforced AC beam specimens during a notched beam fatigue test using the digital image correlation (DIC) technique. The DIC analysis generated full-field displacement and strain contours, depicting the damage mechanisms of geosynthetic reinforcement products in AC overlays under fatigue loading. The quantitative analysis measured the cracked areas caused by each product. The interfacial damage observed in various geosynthetic-reinforced beam specimens under different test conditions suggested that the failure modes of AC beam specimens reinforced with different products varied, and choosing the right product for a project depended on the pavement conditions, geosynthetic product properties, and tack coat properties. Finally, the predictive algorithms presented in this paper allow for predicting the failure mode generated by different geosynthetic products for different overlay conditions in the field, providing valuable insight for pavement engineers. [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]

Geosynthetic reinforcement has proven effective in mitigating reflective cracking in new pavement overlays. This study aimed to compare the ability of five different geosynthetic reinforcement products to alleviate cracking in overlays. Each product was sandwiched within a two-layered asphalt concrete (AC) beam specimen and subjected to a four-point notched beam fatigue test. The study monitored crack propagation, understood crack propagation mechanisms, and quantified crack lengths in different geosynthetic-reinforced AC beam specimens during a notched beam fatigue test using the digital image correlation (DIC) technique. The DIC analysis generated full-field displacement and strain contours, depicting the damage mechanisms of geosynthetic reinforcement products in AC overlays under fatigue loading. The quantitative analysis measured the cracked areas caused by each product. The interfacial damage observed in various geosynthetic-reinforced beam specimens under different test conditions suggested that the failure modes of AC beam specimens reinforced with different products varied, and choosing the right product for a project depended on the pavement conditions, geosynthetic product properties, and tack coat properties. Finally, the predictive algorithms presented in this paper allow for predicting the failure mode generated by different geosynthetic products for different overlay conditions in the field, providing valuable insight for pavement engineers. [ABSTRACT FROM AUTHOR]

For a long time, the idea of fusing synthetic radar imagery (SAR) and higher-resolution panchromatic imagery, with the aim of increasing the spatial resolution of radar imagery, has been conceived as impossible. The challenge is due to the different natures of microwave radiation and visible radiation, where the former is the spectrum of SAR imagery, and the latter is of panchromatic imagery. Average reflection amplitude, which constitutes pixel values on both panchromatic and SAR imagery, is taken as a common framework around which this study proposes a method that enables the translation of an array on a panchromatic spectrum into an array of C-band microwave spectrum in order to achieve a higher spatial resolution. In this regard, open-access Worldview-2 and Worldview-3 panchromatic and Sentinel-1 (amplitude-only) images are used. The outputs are qualified using spatial correlation coefficient (SCC), net histogram curve, and R2 difference, and Erreur relative globale adimensionnelle de synthese (ERGAS) as full-reference image quality assessment methods in various scenarios. The results are tenable enough to consider this method for ordinary earth observation and monitoring objectives. [ABSTRACT FROM AUTHOR]

The testing and evaluation of adhesive bonding quality between thermoplastics are crucial for structural integrity. This article presents the use of phased array ultrasonic testing (PAUT) method to characterise the adhesive interface between thermoplastic composites. Samples with three different bond conditions: control, bad and mid-level were fabricated and tested using PAUT. A damage index (DI) based classification framework aided by machine learning (ML) algorithm is proposed to classify different adhesion conditions. A set of 18 physics-based damage indices were extracted from each PAUT image for quantitative characterisation. ML algorithms were developed to build a non-linear mapping that correlates the input DIs with the output sample types to address the classification problem. The experimental results show that support vector machine (SVM) performs better than other ML algorithms with classification accuracy greater than 95%, and the defined DIs can differentiate among bad, mid-level, and control samples. [ABSTRACT FROM AUTHOR]

In the global navigation satellite system-based synthetic aperture radar interferometry (GNSS-based InSAR) system, 3D deformation retrieval needs to associate multiangle images, but due to the low 2D resolution, traditional texture-based algorithm cannot be used in GNSS-based system. At the same time the association error introduced by the elevation error also cannot be compensated for. In this paper, a multiangle images aggregation analysis and association algorithm is proposed under elevation error conditions. Instead of using texture information, focus positions from different angles are adopted here. First, the focus position with elevation error is modelled in the bistatic configuration. Then, the images association is achieved based on the aggregation analysis, which is further quantitatively expressed through minimum circle principle. Raw data from four satellites are used to demonstrate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image change detection is a key technique for such essential applications as flood disaster assessment and forest fire detection. In SAR image change detection, clustering algorithm is the most applied methods, but clustering algorithm only considers the grey features, therefore it is more susceptible to speckle noise. Deep learning model is difficult to be trained by supervised learning because of lack of labels. To alleviate the above-mentioned challenges of SAR image change detection, a parallel dual-branch SAR image change detection network based on clustering and segmentation (Clustering-Segmentation Network) is proposed in this paper. In the clustering branch, the clustering-based change detection results were obtained by fuzzy c-means (FCM) clustering. In the segmentation branch, the Graph-Based Image Segmentation algorithm was used for pre-segmentation. These results were used as labels of the neural network for training for the segmentation-based change detection. After the fusion of these dual-branch, a double sparse dictionary (DSD) discrimination algorithm is proposed to extract the neighbourhood features for the final discrimination, and obtain the final results. By fusing the dual-branch results, the influence of speckle noise on change detection can be suppressed while maintaining a high accuracy. We show that the Clustering-Segmentation Network exhibited better results compared with existing algorithms on several datasets. The accuracy and kappa coefficients are improved by 0.83% and 3.45% respectively, thereby proving the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

During the last decades, Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been vastly utilized in the military services and civil studies. Because of the sensibility of the SAR images of the imaging in the azimuth dimension, using the multi-aspect SAR image sequence is more practical than the single-aspect SAR image to achieve the superior classification accuracy in an actual SAR ATR task. At present time, multi-aspect SAR ATR models mostly utilize Recurrent Neural Networks (RNN) that depend on the sequence between samples and so suffer from the lack of information. In a practical work, a huge amount of training set is needed to train a deep learning network accurately, but it is so costly work to extract multi-aspect SAR images. So, in this article, a new model of multi-aspect SAR ATR is proposed by using a self-attention model. The proposed self-attention model is utilized to calculate the internal correlation between the original SAR images. At the same time, to develop the anti-noise capability of the proposed model and decrease the dependency on a huge amount of training data, a Convolutional Auto Encoder (CAE) is designed and utilized in the feature extraction section of the proposed model. On the other hand, unlike existing traditional methods, in this work we use both amplitude and phase information of SAR images to devolve the training process of the proposed model. It should be noted that all of the parameters of the proposed network are developed to a complex domain. In addition a complex backpropagation algorithm by using the gradient based model is used for training the network. In the end, experiments is obtained by using two MSTAR data set (the MSTAR-SOC and EOC). The experimental results prove that the proposed model not only can achieve a high recognitions rate on the case of sufficient training sample but also can obtain an acceptable rate in the case of small training samples in different complex situations. In addition, the simulation results demonstrate that by using the encoder of CAE in the proposed model, the whole configuration of the proposed model achieves the anti-noise capability that is a valuable benefit of any practical SAR ATR task. [ABSTRACT FROM AUTHOR]

An oil spill is a major problem for marine life. This spilled oil affects marine life and disturbs the ecosystem. To overcome this problem, SAR sensors have been utilized in marine units to find oil spills because they are free from cloudiness and sun radiation. Even though the oil spill can be detected in SAR images, the processing time and approach are complex. Several techniques have been proposed for the detection of oil spills using matching algorithms, morphological processes, and clustering. But those techniques require multiple processing steps and algorithms for detection. It also depends on user values for detection. Hence, in this paper, the detection of the oil spill in SAR images is automated by utilizing an optimized deep-learning approach. The deep learning approach automatically detects the spilled region using a trained network. Here, the hybrid Moth flame and particle swarm algorithms are used for finding the hyperparameters for training the deep learning network. With this optimized deep learning approach, the oil spill can be detected automatically and its performance verified by implementing the proposed approach in the MATLAB R2020b version. The proposed method's performance is evaluated and compared with the existing technique using accuracy, sensitivity, specificity, Jaccard, and dice. The proposed method produces 99.64% accuracy, which is very high, and only 0.2 classification error, which is very less compared to the existing methods. [ABSTRACT FROM AUTHOR]

Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully polarimetric (FP) and simulated compact polarimetric (CP) C- (RADARSAT-2) and L- (ALOS-2 PALSAR-2) band SAR, in the 2018 melt season in the Canadian Arctic Archipelago, for stage-wise separation of first year ice (FYI) and multiyear ice (MYI). SAR scenes at both near- (19.1–28.3°) and far- (35.8–42.1°) range incidence angles and coincident high-resolution optical scenes were used to assess the impact of surface melt ponds on separability within a landfast ice zone of diverse ice thickness. C-band provided better separability between FYI and MYI during pond onset, while L-band was superior during pond drainage due to MYI volumetric scattering. CP parameters matched FP performance across the melt season. HH and HV, commonly offered in ScanSAR mode for both frequencies, presented good separability during pond onset and drainage. Using both C-band and L-band SAR along with constraining incidence angle ranges, enhances sea ice type identification and separability. Our results can support ice type classification and seasonal stage detection for climate studies and enhance existing frameworks for ice motion vector retrievals. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) images are classified mainly according to the backscattering information of ground objects. For regions with complex backscattering information, misclassification is easy to occur, which leads to challenges in improving the classification accuracy of the PolSAR image. Given this situation, this paper combines the Deep Learning Model and traditional classifiers to classify PolSAR image. First, the Convolution Neural Network (CNN) was used to classify the PolSAR image and according to the category prediction probability of pixels, the key pixels easily misclassified are located. Then, the adaptive boosting (AdaBoost) algorithm combined the three shallow classifiers (the Support Vector Machine (SVM), the Wishart and the Decision Tree classifier) into strong classifiers to reclassify the key pixels. Finally, the labels of key pixels and other pixels are output as the final classification result. Experiments on two PolSAR images show that the proposed method can improve classification performance and obtain better classification results. [ABSTRACT FROM AUTHOR]

Automatic despeckling from synthetic aperture radar (SAR) imagery plays a significant role in many urban applications. Recently, owing to the impressive performance of deep learning, various SAR despeckling methods based on the convolutional neural network (CNN) have been proposed for optical SAR images. However, existing CNN-based despeckling methods and spatial-domain-based methods exhibit certain limitations. The introduction of prior knowledge is not considered in the designing of classical denoising and certain CNN-based methods, thus leading to the loss of edge information and artefacts in the results. To address this issue, an a priori-based deep neural network SAR image despeckling method (SAR-PBDNN) was proposed. In this approach, the Bayesian formula is modelled to calculate the objective function suitable for the despeckling problem and is optimized using the alternating direction method of multipliers algorithm and deep learning method. The introduction of prior information effectively improved the denoising effect of SAR images. The SAR-PBDNN was evaluated using both simulated and real SAR images. The comparisons demonstrate the effectiveness of the proposed method. We conclude that the SAR-PBDNN has the potential to automatically despeckle SAR images with an accuracy that renders it a useful tool for practical application scenarios. [ABSTRACT FROM AUTHOR]

Green macroalgae bloom (GMB), with the dominant species of Ulva prolifera, has regularly occurred since 2007 along the China coast. Although disaster prevention and control achieved favorable results in 2020, the satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an unprecedented level. The reasons for this rebound and the significant interannual variability over past 15 years are still open questions. Here, by using long-term time-series (2007–2022) optical and Synthetic Aperture Radar satellite observations (1000+ scenes), meteorological data and water quality statistics, the mechanism analysis was performed by exploring effects from natural factors and human activities. Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area (the Subei Shoal) during a critical period (from April to May 20) and the nutrient availability. Furthermore, by using these two parameters, a novel model for AMC prediction (R2 = 0.87, p < 0.01) is proposed and independently validated, which can reasonably explain the significant interannual variability (2014–2021) and agree well with the latest observation in 2022 (percentage difference 12%). Finally, suggestions are proposed for future disaster prevention and alleviation. This work may aid future bloom prediction and management measure optimization. [ABSTRACT FROM AUTHOR]

The reliability of the model-based decomposition result, which is seldom investigated, is a key factor in determining whether decomposition parameters can be effectively applied to polarimetric synthetic aperture radar (PolSAR) applications. In this paper, a type of polarimetric parameter for evaluating the reliability of model-based decomposition result is proposed. It originates from the degree of correlation between the scattering models. The main idea is whether the measured power can be assigned to the corresponding scattering model. The more the measured power that can be allocated to the corresponding scattering model, the more reliable the decomposition result. The C-band Radarsat-2, L-band UAVSAR, X-band TerraSAR-X, and L-band E-SAR PolSAR data are selected for experiments. The results show that the type of polarimetric parameter can effectively represent the reliability of the decomposition result. The reliabilities of surface and double-bounce scattering are high in the ocean and orthogonal buildings, respectively. For oriented buildings, the reliability of the decomposition result is lower owing to the overestimation of volume scattering. For forested areas, reliability is generally higher if volume scattering is not overestimated. Furthermore, the results show that the reliability of soil moisture retrieval is positively correlated with surface scattering reliability. [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]

Monitoring the subsidence at different periods in high underground water level coal mine areas can provide a data basis for land reclamation. For the purpose of monitoring subsidence in I working face located in Guotun Coal Mine, 22 Sentinel-1 images were processed by using differential interferometric synthetic aperture radar (D-InSAR). After dividing subsidence periods, the characteristics and monitoring accuracy at different subsidence periods were explored. The results showed that: (1) The formation of single-gradient subsidence funnel is an important sign to identify active period. (2) The root mean square error (RMSE) and mean absolute error (MAE) in central area of subsidence are reduced by 50% and 49%, respectively, compared with those in edge area. (3) The monitoring accuracy is high at different subsidence periods. The coefficient of determination (R2) can reach more than 0.89. Therefore, D-InSAR technique can accurately monitor the subsidence at different periods based on Sentinel-1 data. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) images contain rich information about back-scattering and physical characteristics of targets. So, they have high ability for discrimination of different land cover classes. The aim of this research is to introduce an efficient method for PolSAR image classification. Extraction of both scattering and contextual features is important for class discrimination. Therefore, the scattering and contextual feature fusion (SCF) method is proposed to fuse the extracted polarimetric and morphological features through applying a complex multi-scale decomposition. The dual tree complex wavelet transform is used to decompose each scattering feature map into its details and approximate components. The contextual feature maps are decomposed in a similar way. Then, details of two kinds of feature maps are fused region by region. This process is also done for the approximation components containing the low frequency information. The result will be a high dimensional fused feature space. The principal discriminant analysis (PDA) is proposed to reduce the data dimensionality with discarding noisy components and increasing the class discrimination. The extracted features are then fed into a simple classifier to obtain the classification map. Three L-band PolSAR images acquired by airborne synthetic aperture radar (AIRSAR) and electronically steered array radar (ESAR) are used for doing experiments. The SCF method shows superior classification results with respect to several state-of-the-art PolSAR classifiers. For example, for the Flevoland dataset containing 15 classes, without applying post processing, the SCF method results in 95.22% overall accuracy compared to 2DCNN with 91.84% and 3DCNN with 93.94% overall accuracy. With applying post processing, the classification results of SCF, 2DCNN and 3DCNN are increased to 99.55%, 98.61% and 99.09%, respectively. [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]

There are two types of features in a polarimetric synthetic aperture radar (PolSAR) image: 1-physical scattering characteristics of radar, and 2-geometric and texture properties. To handle the 3D nature of PolSAR image, a revised 3DGabor filter bank is proposed here. Although the 3DGabor filter can explore the interaction between three dimensions of the PolSAR cube, but it does not utilize the physical information of radar including the scattering mechanism. To deal with this issue, the physical parameters of the entropy/anisotropy/alpha decomposition theory are used to revise the 3DGabor filter. The output is joint texture and physical features of the PolSAR cube. These features are highly discriminative such that they can provide high accurate classification maps specially by using limited training samples. The experimental results on three PolSAR images show better performance of the proposed method with respect to several competitors with a significant difference from the statistical point of view. [ABSTRACT FROM AUTHOR]

Persistent scatterers synthetic aperture radar interferometry (PSInSAR) technology has been applied in the context of deformation monitoring of transportation infrastructures such as bridges, highways, and railways. However, extracting and interpreting information from long-time series displacement obtained using PS-InSAR is a complex and demanding task. In this study, time series analysis was used to post-process the displacement time series derived from PSInSAR using 100 stripmap mode images collected by TerraSAR-X satellite during 13 April 2010 and 13 December 2019. Seasonal and trend decomposition using loess (STL) and variational modal decomposition (VMD) were combined to estimate the different periodic and trend components of the total displacement time series. Temperature correlation was then generated by associating the different period components with the temperature change. The results show that the deformation of Sihui overpass, Sifang overpass and Shibalidian overpass in Beijing Fourth Ring Road highway is obvious. The trend components obtained by STL decomposition of the selected typical points are primarily nonlinear deformation, and the deformation of Sifang overpass, Shibalidian overpass and Liuxiang overpass slows to varying degrees. After decomposing the displacement time series into several intrinsic mode functions (imf) with finite bandwidth using VMD, the thermal expansion phenomenon is pronounced on the main bridge and approach bridge of some overpasses. Seasonal imf1 is an annual periodic that appears to be highly positively correlated with temperature change. Seasonal imf2 is a period term of greater than one year that confirms that the bridges exhibit other periodic seasonal components in addition to the seasonal deformation of annual period. The thermal expansion characteristics of different periodic seasonal components that are explored in this study can provide valuable information for better understanding the thermal expansion evolution process, and these findings are important scientific significance for bridge structure health monitoring. [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]

Synthetic Aperture Radar (SAR) imagery can be beneficial for segmenting oil spills, which are a common environmental hazard. Oil spill detection in SAR imagery faces several challenges, including speckle noise, heterogeneous backgrounds, blurred edges, and a lack of comprehensive datasets with multiple images. ShuffleNet is one of the deep networks, which has never been used for oil spill segmentation. In this article, ShuffleNet blocks are used to detect oil spills in SAR images, which is more effective than other methods. Besides, the main network design, six other blocks were evaluated, and the most valuable one was selected. We use group convolutions, shuffle channels, and atrous convolutions in this model with a minimum number of layers of ReLU. The methods are evaluated based on the Intersection Over Union (IoU) parameter so that the proposed method improved the mIoU by 7.1% over the best results of some previous methods. [ABSTRACT FROM AUTHOR]

Targeted on accurate and efficient ship detection of Synthetic Aperture Radar imagery, sea-land segmentation is key for the complex port scenes. Despite the importance of this task, current methods are still severely influenced by several limitations, such as high level of image speckle noise, complicated backscattering characteristics. In order to overcome these issues, we propose a sea-land segmentation method of Synthetic Aperture Radar imagery based on multi-source data registration. To be specific, high-resolution optical remote sensing images and high-precision coastline vector data are integrated with Synthetic Aperture Radar images for accurate sea-land segmentation. First, the Pyramid Level-by-level Guided-Histogram of the Orientated Phase Congruency method is developed for the registration of Synthetic Aperture Radar and optical images, making two types of images into the same spatial reference. Following this, the high-precision coastline vector data collected from optical images is transferred to the Synthetic Aperture Radar image, realizing accurate sea-land segmentation. Finally, aiming at ship detection tasks, two quantitative evaluation indicators for sea-land segmentation are proposed: (1) the Absolute Segmentation Accuracy; and (2) the Ship Mis-segmentation Index. The accuracy and efficiency of the proposed method are tested and verified on Gaofen-3 SAR images of Yokosuka Port, Japan. Experimental results show that the proposed method achieves an Absolute Segmentation Accuracy of 3.15 pixels, a Ship Mis-segmentation Index of 9.23% and processing time of 82.92 s on average, which reveals that the proposed method outperforms current popular approaches in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Traditional crop cutting experiment-based yield estimation method captures the regional yield variability but lacks field-level information. Satellite images hold enormous crop information at finer spatial resolution. Crop yield mapping with optical images is particularly challenging if cloud-free images are unavailable during the crucial crop developmental stages. All-weather availability and sensitivity to crop structure, dielectric properties make synthetic aperture radar (SAR) images an excellent resource for yield estimation. Both types of data provide complementary information about crop conditions. A random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation. We utilized the crop harvest and quality survey (BEE) yield data set collected by the Hessisches Statistisches Landesamt (HSL), Wiesbaden, Germany, over 490 fields. We prepared 20m resolution yield maps for winter wheat, winter barley, winter rye and winter rapeseed. Input features for the yield estimation model are selected based on the prior knowledge of remote sensing of vegetation. Baseline random forest regression models are developed for all the four crop types with optical and SAR input features. An optimized random forest regression model with genetic algorithm-based feature selection results in performance improvement. Dissimilarity in genetic algorithm selected image features highlights the significance of crop-specific feature selection for yield estimation. The optimized models reliably estimate yield by achieving correlation coefficient (r) of 0.65-0.86, mean absolute error 0.93-1.16 t ha-1 and root mean square error 1.12-1.56 t ha-1 with BEE yield on testing data set. The proposed models could estimate the intra-field yield variation when winter wheat, winter barley, winter rye were in the shooting phase to the beginning of ear-shifting, and winter rapeseed began to flower or was already flowering. These results demonstrate the merits of our model for early-stage crop yield estimation at the field level with mono-temporal image and adaptability for the cropping season with high cloud cover. [ABSTRACT FROM AUTHOR]

High transmit-receive isolation between any two adjacent elements in sequentially switched linear arrays guarantees the performance of high-resolution close-range imaging. However, the transmit-receive antenna spacing required in wavelength order makes its isolation deteriorated rapidly in the millimeter wave and higher frequency bands. In this paper, we propose a simple yet effective method to improve transmit-receive isolation by a switch sequence adjustment on the traditional linear arrays. The new switch-mode aims to simply increase the vertical spacing between each transmit-receive pair. To maintain the densely uniform-spatial effective sampling and Fourier transform-based image reconstruction, the principle of equivalent phase center is utilized to analyze the maximum of the possible vertical spacing of each transmit-receive pair is four wavelengths. Experiments verify that the method can efficiently improve transmit-receive isolation by up to 11.5 dB and guarantee the high performance of MMW imaging. [ABSTRACT FROM AUTHOR]

The dual polarimetric study including degree of polarization ( m L ) and energy span (λ1+ λ2) for vegetation targets infer the accuracy of vegetation algorithms. The Sentinel − 1 A and ALOS − 2 satellite data were utilized for dual polarimetric radar vegetation index (DPRVI), polarimetric radar vegetation index (PRVI) and radar vegetation index (RVI) computation and simulation of backscattering coefficient (σ0 (dB)) at VV polarization. The DPRVI, PRVI and RVI were computed at VV + VH and HH + HV polarizations, respectively. The simulation of σ0 (dB) at HH + HV can be show better results than VV + VH polarization using derived radar vegetation descriptors (V). The DPRVI, PRVI and RVI showed higher R2 and lower RMSE values at L – band than C - band. The DPRVI at L – band (HH + HV polarization) was indicated highest R2 = 0.91 and lower RMSE = 0.31 (dB) among all proposed V in the algorithm. [ABSTRACT FROM AUTHOR]

Sea ice detection has played an important role in climate protection and strategic deployment. Due to the autonomous learning characteristics of deep learning, it has been gradually applied to the classification of remote sensing sea ice images. At present, deep learning models are mostly used in sea ice classification of single source remote sensing data. Due to the limitations of single source data and the information loss of deep learning model in the process of feature extraction layer by layer, it is inevitable to encounter a bottleneck in sea ice detection requiring fine classification. To solve the above problems, this paper proposes a sea ice image classification method based on ResNet16-feature pyramid networks-spatial pyramid pooling-gated fusion network (ResFPG) and heterogeneous data fusion. In the feature extraction part, the method uses the improved ResNet16 to extract the multi-level feature information of sea ice in synthetic aperture radar data and optical data, reducing information loss in the feature extraction process, then mines and fuses the low-level spatial information and high-level semantic information through the improved feature pyramid networks (FPN), and then collects and fuses the output features of different scales through the spatial pyramid pooling (SPP) network. In the feature fusion part, a gated feature-level fusion strategy is designed to further improve the overall classification accuracy by adaptively adjusting the feature contribution of two heterogeneous sources of data through the gated fusion network (GFN). In order to verify the effectiveness of this method, we use two sets of heterogeneous sea ice remote sensing data located in Hudson Bay area for experiments. The experimental results show that compared with other image classification methods, the proposed method fully excavates and integrates the multi-scale and multi-level features in heterogeneous data, effectively distinguishes the feature contribution, and achieves better classification results (97.14% and 95.85%). [ABSTRACT FROM AUTHOR]

Precise and regularly updated maps of surface water extent are essential for wetland management. Since it is often challenging to obtain water extent information through ground surveys due to accessibility, satellite remote sensing has become a critical and cost-effective tool for acquiring this information in a temporal context. The methods that are commonly used include supervised and unsupervised multispectral classification, density slicing of a single band and spectral indices. However, optical data are substantially affected by cloud cover and solar illumination, thus, radar data comprise an ideal option. This article presents a methodological framework for rapid delineation of waterbodies' boundaries. For that purpose, an object-based approach is implemented. Sentinel-1 data were obtained and the Mean-Shift segmentation algorithm was employed. The proposed methodology produced promising results achieving an overall accuracy of 98%, a producer's accuracy for waterbodies of 90% and a user's accuracy for waterbodies of 95%. [ABSTRACT FROM AUTHOR]

The present work has focussed on the application of Cinnamon mucilage as a natural flocculant for the removal of Red FBB from synthetic solution using coagulation/flocculation technique. Characterisation of Cinnamon mucilage was performed by Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX), X-ray diffraction (XRD) and Fourier transform-infrared spectroscopy (FTIR). The effects of the main experimental conditions (pH of the solution, coagulant and flocculant dosage, etc.) on the flocculation treatment performance were studied. The optimum conditions for highest dye removal (>94% for both coagulants) are: pH 6.0, 30 mg/L coagulant dosage, 2 ml/L flocculant dosage, 200 rpm fast mixing, 45 rpm slow-mixing, 15 min mixing time, and 30 min settling time. The sludge volume index (SVI) values obtained were inferior at 55 mL/g. A statistical analysis using one-way analysis of variance (ANOVA) displayed no significant difference between Cinnamon, Flaxseed and Plantago Psyllium mucilage. [ABSTRACT FROM AUTHOR]

This paper describes a dual circularly polarized (CP) antenna with high isolation and its 2 × 2 element array for the X-band synthetic aperture radars. The antenna is an aperture-coupled metasurface of 4 × 4 periodic corner-truncated patches, which is fed by two orthogonal microstrip-lines to generate the dual-CP radiation. A novel asymmetric fence-shaped slot is utilized as the coupled aperture of the antenna to enhance the port-to-port isolation. To achieve a more robust dual-CP radiation, the 2 × 2 element array of the aperture-coupled metasurface antenna is designed, fabricated, and tested. Both simulation and measurement indicate that the array prototype achieves an excellent dual-CP radiation at 9.6 GHz, i.e., isolation of ≥ 30 dB, axial ratio ≤ 1-dB, radiation efficiency ≥ 80%, realized gain of 14.0 dBic, and cross-polarized level ≤ −20 dB at the broadside. [ABSTRACT FROM AUTHOR]

Corrosion is a serious affliction for reinforced concrete (RC) structures and the development of advanced inspection techniques is crucial for maintaining serviceability and durability. In this study, a novel proof-of-concept is presented to monitor corrosion in the concrete by employing contact-based ultrasonic excitation and non-contact laser-based sensing. Accelerated corrosion of rebars in RC slabs is implemented, and ultrasonic scanning is performed on specimens at various corrosion levels. The excitation source is a contact ultrasonic transducer, which enables better penetration of the waves into the concrete medium. For the reception, a Doppler laser vibrometry based sensing is used, which expedites the inspection by circumventing the requirement of coupling. The Synthetic Aperture Focusing Technique (SAFT) is employed for subsurface image reconstruction. Rebar images obtained by hybrid sensing technique, are qualitatively similar to those obtained via traditional contact-based source and receiver system. A histogram-based qualitative analysis is presented to correlate the characteristics of the rebar images to the progress of corrosion. A planar SAFT imaging algorithm is presented to demonstrate the rebar on a horizontal plane for better visualisation. The proposed technique is significantly faster in comparison to the contact-based approach and, therefore, has the potential of being applied for inspection of larger concrete infrastructure. [ABSTRACT FROM AUTHOR]

Food insecurity is one of the major challenges facing African countries; therefore, timely and accurate information on agricultural production is essential to feed the growing population on the continent. A synergistic approach comprising a high-resolution multispectral UAV optical dataset and synthetic aperture radar (SAR) can help understand spectral features of target objects, especially with crop type identification. We conducted this work on the experimental plots using high spatial resolution multispectral UAV data (12 cm, re-sampled to 50 cm) in combination with the Sentinel 1C Synthetic Aperture Radar (SAR) dataset. We generated 11 agronomically relevent vegetation indices from the UAV multispectral image. Multiple combinations of the UAV datasets were analysed to assess the impact of canopy height model (CHM) on classification accuracy and to determine the optimum dataset (including spatial resolution) for the land cover classification. We also appraise the impact of variable spatial resolution on classification accuracy. A combination of VH and VV polarizations of Sentinel-1 SAR data was also analysed to classify the crop types while comparing its accuracy with the UAV-derived models. Our results show that model accuracy is improved- for all the data combination pairs, when CHM is added to the modelling. We also observed a decreasing trend in classification accuracy with respect to increasing spatial resolution. Generally, the support vector machine (SVM) classifier produced an overall accuracy of 94.78% and 81.72% for UAV and SAR datasets, respectively. In comparison, the random forest (RF) achieved an accuracy of 93.84% and 92.58%, for UAV and SAR datasets, respectively. The outputs from ground-based validation corroborate the results from model-based classification coupled with acceptable simple models' agreement ratio (SMAR), exceeding 90% in some cases. The combined techniques can be useful in precision agriculture over small and large agricultural fields to support food security assessment and planning. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) came into practical use as a geohazard monitoring tool in the 1990s. The uptake of this remote sensing technique however is geographically varying, and Australia has been one of many continents not fully utilizing the available InSAR data. As a result, limited knowledge exists on the application of InSAR data to obtain insights and benefits in geohazard risk monitoring and management. Due to the said limitation, this research carried out a concise technical background study and investigated a suite of case studies to demonstrate the potential for geohazard monitoring and assessment in Australia using InSAR. The differential InSAR workflow was applied to sites that experienced heaving and subsiding of the ground surface, mining, earthquake, landslides, and tunnelling in West Metro Melbourne (VIC), Musgrave (SA), Crinum mine (QLD), Pelham (TAS), Lake Muir (WA), and Sydney (NSW). The results of the Differential InSAR analysis resulted in acceptable ground movement and deformation estimates when compared with measured field data. Future opportunities related to new applications of the InSAR technique on geohazard reduction considering climate change contributions and probable coupling with artificial intelligence techniques were also discussed. This research has formed a conducive and scientific learning medium, which may encourage the growth of multidisciplinary research combining the fields of remote sensing and geotechnical engineering to investigate the impact of geohazards. [ABSTRACT FROM AUTHOR]

Empirical wind-retrieval algorithms for synthetic aperture radar (SAR), such as the geophysical model function (GMF) CMOD5N in the C-Band, are currently well developed. The retrieval accuracy of GMFs, however, is lower in the presence of marine phenomena such as oceanic fronts and mesoscale eddies, making SAR wind retrieval challenging under these conditions. In this study, we proposed a scheme for wind retrieval from Chinese Gaofen-3 (GF-3) SAR images using an analytical approach to obtain the wind speed in the nearshore waters of China's seas. Approximately 300 images were acquired from 2018 to 2021 in the copolarization channels [vertical–vertical (VV) and horizontal–horizontal (HH)]. The images were collocated with the measurements from the Advanced Scatterometer (ASCAT) and the Chinese Haiyang-2B (HY-2B) scatterometer. Our analytical approach was based on the Bragg resonant and non-Bragg (NB) scattering model for the calculation of the normalized radar backscatter cross-section (NRCS), which accounted for the influence of wave-current interactions and breaking waves in nearshore waters. We used the sea surface currents obtained from the HYbrid Coordinate Ocean Model (HYCOM) to simulate the Bragg resonant roughness. We derived the contribution of the NB breaking waves from the SAR-measured NRCS values in both the VV and HH channels. By comparing the retrieval results obtained using the analytical approach with the scatterometer products, we concluded that the root mean square error (RMSE) of the wind speed was 2.01 m s−1 with a 0.91 correlation coefficient (COR), which was less than the 2.93 m s−1 RMSE and 0.72 COR for the GMF CMOD5N. The variation in the bias of the HYCOM currents was approximately 1 m s−1 for current speeds greater than 0.3 m s−1. These findings verified the stability of the analytical approach for SAR wind retrieval in complex sea states. [ABSTRACT FROM AUTHOR]

A feature space projection based on the nearest polarimetric and spatial neighbours is proposed for polarimetric synthetic aperture radar (PolSAR) images. The objective function is designed such that increases the class discrimination while preserves the contextual properties. Each polarimetric or spatial neighbour is contributed proportional to with its Wishart distance to the given pixel. A guidance image, which involves all polarimetric channels, is also introduced for spatial filtering of the initial classification map, which is efficient for improving the final classification map. The proposed method shows high performance for PolSAR image classification using limited training samples. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) image classification is a significant task of PolSAR applications. In recent years, deep learning-based methods using spatial features have achieved satisfactory results in PolSAR classification. However, there are still some important features left to be exploited. One is the target scattering orientation diversity feature that contains rich hidden information. In this letter, a hybrid network is proposed to model the target scattering orientation diversity feature and spatial feature. First, the polarimetric scoherency matrix is expanded to polarization coherency matrix sequence by different polarization orientation angles (POAs). Then, the long short-term memory (LSTM) network and the convolutional neural networks (CNN) are proposed to process the polarization coherency matrix sequence and the square neighbourhood of pixels, respectively. Finally, the features of the two sub-networks are combined to improve the classification accuracy. The experiments demonstrate that the proposed method can obtain superior and robust performance. [ABSTRACT FROM AUTHOR]

Crop biophysical parameters, such as Leaf Area Index (LAI) and biomass, are essential for estimating crop productivity, yield modeling, and agronomic management. This study used several features extracted from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and spectral vegetation indices extracted from Sentinel-2 optical data to estimate crop LAI and wet and dry biomass. Various machine learning algorithms, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were trained and assessed for three major crops (wheat, soybeans and canola). ANN provided the best accuracy for all wheat parameters and soybean LAI and canola wet biomass and LAI. RFR led to higher accuracy for soybean dry and wet biomass. However, SVR could accurately estimate only canola dry biomass. All data were then pooled to investigate if a single algorithm could estimate biophysical parameters for all crops. The RFR model accurately estimated wet and dry biomass and LAI across all crop types in this scenario. This generic model is fast and accurate and can be easily applied for crop mapping and monitoring over large geographies using cloud computing platforms, such as Google Earth Engine. [ABSTRACT FROM AUTHOR]

One of the steps in the construction of thematic products is the categorization. This process is done by partitioning the range of values of a given feature into several subranges and associating a new value, in this case ordinal, to all instances that are in that subrange. The objective of this work is to propose an innovative pre-categorization process that applies a computational search method to maximize the accuracy of thematic products during the classification stage. The theme feature used is the Above Ground Biomass (AGB), which estimation model is built over synthetic aperture radar features. A system is developed in Java script using the Weka data mining class library. The proposed heuristic is the hill climbing greedy, with the Kappa coefficient as the objective function. The results obtained shows that the proposed Categorization Optimization algorithm demonstrated the ability to obtain new states with subintervals of categories that increased the Kappa agreement index to 1.0 with much lower computational cost than the exhaustive search. The thematic products constructed maintained the representativeness of the study area while increasing in thematic accuracy. [ABSTRACT FROM AUTHOR]

Multitemporal interferometric synthetic aperture radar (MT-InSAR) technique is an important approach for surface deformation monitoring. Nonlocal adaptive multilooking approach has potential benefits for the filtering and coherence estimation in the data preprocessing steps of MT-InSAR technique. The kernel of nonlocal adaptive multilooking approach lies in the selection of statistically homogeneous pixels (SHPs). Various amplitude-based strategies, such as DespecKS and its variations, have been proposed for selecting SHPs. However, the detection rates of these methods are usually unsatisfactory in the case of small data sets. To overcome this limitation, SHPs are selected based on the adaptive joint data vector, which encompasses both time dimensional samples and spatial information. In this letter, the outliers of the time dimensional samples are removed first based on a revised boxplot method. Additionally, image segmentation and L1 norm are both introduced to adaptively construct joint data vector in the preset window. Experiments on five TSX images are used to verify the reliability and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Yellow Rail (Coturnicops noveboracensis) are a highly specialized wetland obligate bird. They are a species at risk in Canada and very little is known about their abundance in the wetlands of the western boreal forest. Emerging technologies have enabled us to effectively survey for Yellow Rail and other wetland birds in remote areas by using ground-based remote sensors (autonomous recording units; ARUs) to conduct passive acoustic monitoring. We analyzed bird data from the first four years (2013–2016) of an ongoing monitoring program led by the Bioacoustic Unit at the Alberta Biodiversity Monitoring Institute. We developed species abundance models using satellite data from Sentinel-1 and Sentinel-2 processed in Google Earth Engine. We identified covariates from both synthetic aperture radar and optical remote sensing that had strong predictive capacity for this wetland bird (AUC = 0.96). Approximately 1.5% of available wetland habitat in our northeast Alberta study area was predicted to be highly suitable for Yellow Rail. [ABSTRACT FROM AUTHOR]

A broadband aperture-coupled asymmetrical fractal tree-shaped microstrip antenna is proposed with the mirror image ground for coupling. A resonant coupling slot in the ground is provided which is the mirror image of the radiating patch. The result shows that there is an increase in the bandwidth of antennas compared to the basic design. The proposed design for multiband application covers YiMAX (i.e. 2.5 GHz to 2.69 GHz) and the entire UWB frequency range (i.e. 3.1 GHz to 10.6 GHz). The proposed antenna is printed using two FR4 substrates with a relative permittivity of 4.4. The thickness of the structure is 1.6 mm and the patch size is 70 × 106 mm. The novelty of the proposed antenna is it is designed using a fractal structure with the modified ground with mirror slot. The asymmetrical structure for the fractal tree using feeding with aperture coupling is used to design the proposed antenna. The proposed antenna is designed using the HFSS software. The fabricated antenna is tested on the Agilent vector network analyzer. [ABSTRACT FROM AUTHOR]