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International audience; Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

Peer-reviewed journals are a pillar of modern science. Their aim is to achieve highest scientific standards by carrying out a rigorous peer review that is, as a minimum requirement, supposed to be able to identify fundamental methodological errors or false claims. Unfortunately, as many climate researchers and engaged observers of the climate change debate pointed out in various internet discussion fora, the paper by Spencer and Braswell [1] that was recently published in Remote Sensing is most likely problematic in both aspects and should therefore not have been published. After having become aware of the situation, and studying the various pro and contra arguments, I agree with the critics of the paper. Therefore, I would like to take the responsibility for this editorial decision and, as a result, step down as Editor-in-Chief of the journal Remote Sensing. [...]

Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for the year 2014. [...]

Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for 2013. Nominations were selected by the Editor-in-Chief and selected editorial board members from among all the papers published in 2009. Reviews and research papers were evaluated separately. [...]

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.

For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM and building footprints are sometimes of a low accuracy, thus generating low-accuracy building models. Though some recent studies have added GIS data to refine the contour of the building footprints, the registration errors between the GIS data and satellite images are not considered. Since OpenStreetMap (OSM) provides a high level of precision and complete building polygons in most cities worldwide, this paper proposes an automatic single building reconstruction method that utilizes a DSM from high-resolution satellite stereos, as well as building footprints from OSM. The core algorithm accurately registers the building polygons from OSM with the rasterized height information from the DSM. To achieve this goal, this paper proposes a two-step “coarse-to-fine registration” algorithm, with both steps being formulated into the optimization of energy functions. The coarse registration is optimized by separately moving the OSM polygons at fixed steps with the constraints of a boundary gradient, an interior elevation mean, and variance. Given the initial solution of the coarse registration, the fine registration is optimized by a genetic algorithm to compute the accurate translations and rotations between the DSM and OSM. Experiments performed in the Beijing/Shanghai region show that the proposed method can significantly improve the IoU (intersection over union) of the registration results by 69.8%/26.2%, the precision by 41.0%/15.5%, the recall by 41.0%/16.0%, and the F1-score by 42.7%/15.8%. For the registration, the method can reduce the translation errors by 4.656 m/2.815 m, as well as the rotation errors by 0.538°/0.228°, which indicates its great potential in smart 3D applications.

High-resolution remote-sensing images cover more feature information, including texture, structure, shape, and other geometric details, while the relationships among target features are more complex. These factors make it more complicated for classical convolutional neural networks to obtain ideal results when performing a feature classification on remote-sensing images. To address this issue, we proposed an attention-based multiscale max-pooling dense network (DMAU-Net), which is based on U-Net for ground object classification. The network is designed with an integrated max-pooling module that incorporates dense connections in the encoder part to enhance the quality of the feature map, and thus improve the feature-extraction capability of the network. Equally, in the decoding, we introduce the Efficient Channel Attention (ECA) module, which can strengthen the effective features and suppress the irrelevant information. To validate the ground object classification performance of the multi-pooling integration network proposed in this paper, we conducted experiments on the Vaihingen and Potsdam datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). We compared DMAU-Net with other mainstream semantic segmentation models. The experimental results show that the DMAU-Net proposed in this paper effectively improves the accuracy of the feature classification of high-resolution remote-sensing images. The feature boundaries obtained by DMAU-Net are clear and regionally complete, enhancing the ability to optimize the edges of features.

Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and damage assessment. In recent years, hyperspectral remote sensing technology has become an effective means to monitor marine oil spills. The spectral and spatial features of oil spill images at different levels are different. To accurately identify oil spill types and quantify oil film thickness, and perform better extraction of spectral and spatial features, a multilevel spatial and spectral feature extraction network is proposed in this study. First, the graph convolutional neural network and graph attentional neural network models were used to extract spectral and spatial features in non-Euclidean space, respectively, and then the designed modules based on 2D expansion convolution, depth convolution, and point convolution were applied to extract feature information in Euclidean space; after that, a multilevel feature fusion method was developed to fuse the obtained spatial and spectral features in Euclidean space in a complementary way to obtain multilevel features. Finally, the multilevel features were fused at the feature level to obtain the oil spill information. The experimental results show that compared with CGCNN, SSRN, and A2S2KResNet algorithms, the accuracy of oil type identification and oil film thickness classification of the proposed method in this paper is improved by 12.82%, 0.06%, and 0.08% and 2.23%, 0.69%, and 0.47%, respectively, which proves that the method in this paper can effectively extract oil spill information and identify different oil spill types and different oil film thicknesses.

Height measurement of meter wave radar is a difficult and important problem. This paper studies the height measurement of meter wave polarimetric (MWP)-MIMO array radar under complex terrain. The traditional electrically short dipole has low radiation efficiency, and the collocated dipole vector antenna has strong mutual coupling. This paper proposes to use electrically long dipoles and separated vector antennae to solve the problems of low radiation efficiency and strong mutual coupling. In addition, different from the traditional flat terrain, the research of this paper is based on the conditions of complex undulating terrain. First, the height measurement signal model of the MWP-MIMO radar with separated electrically long dipole under the complex terrain is derived. Then, a preprocessing method of block orthogonal matching pursuit is proposed to obtain the coarse estimation of the target’s elevation. Then, under the guidance of the coarse estimation, the generalized MUSIC algorithm is used to obtain the high-precision elevation estimation of the target, and then the height measurement of the target is obtained according to the geometric relationship. Finally, the effectiveness of the proposed algorithm is proved by computer simulations.

Non-cooperative targets, such as space debris, defunct spacecrafts and LEO constellation satellites, have brought serious risks to the space station. The rapid detection and orbital parameters’ determination of the fast-approaching non-cooperative target can greatly improve the protection ability of the space station. In this paper, a novel rapid detection and orbital parameters’ determination method based on the collaborative observation of the space station, and a fly-around nano-satellite is developed. The early-warning region of the space station is established, and considering the observation constraints of the nano-satellite, the non-cooperative target detection strategy is provided, which includes a collaborative observation configuration as well as the attitude variation of the cameras, and the detection efficiency is analyzed. Then, the orbital parameters’ filtering model of the non-cooperative target based on the collaborative observation is constructed, and the Unscented Kalman filter method is utilized to determinate the orbital parameters of the non-cooperative target. Considered the observability of the initial collaborative observation configuration, this paper analyzes the observation configuration with low observability in different scenarios, and proposes an optimal orbital maneuver algorithm for the nano-satellite. This algorithm can realize a fuel-optimal orbital maneuver that satisfies the minimum line-of-sight angle constraint of the collaborative observation.

In this paper, we present a new approach to subpixel infrared (IR) emitter detection in VIIRS mid-wave (MWIR) infrared bands M12–M13 at night, based on the presence of a tightly clustered background diagonal present in full granule scattergrams of M12 versus M13 radiances. This diagonal is found universally in night-time VIIRS data collected worldwide. The diagonal feature is absent during the day due to solar reflectance. The existence of the diagonal is attributed to close spacing in the bandpass centers of the VIIRS’ two MWIR bands. Apparently, the M12 and M13 emissivities are highly correlated to background objects, such clouds, ocean and land surfaces. The VIIRS Nightfire (VNF) algorithm detects pixels containing IR emitters based on their departure from the background diagonal. This paper outlines the method and compares VNF results with those from MODIS and VIIRS hotspot detection.

The alpine inland lake dynamics have been good indicators of changes in terrestrial hydrological cycles under global climate change. However, the relationship between alpine inland lake and climatic factors remained largely uncertain. This study examines the spatial-temporal change of the fluctuation of the lake by using dense time series Landsat TM/ETM/OLI images to delineate water boundary information based on the Random Forest algorithm and using ICESat (Ice, Cloud and land Elevation Satellite) dataset to monitor changes in variations of water level. Variations of Qinghai Lake (QHL) were analyzed from 1987 to 2020 and the mechanism of these changes was discussed with meteorological data. The results indicated that the QHL fluctuated strongly showing a pattern of shrinkage–expansion over the last three decades. The lake storage significantly decreased by −2.58 × 108 m3·yr−1 (R2 = 0.86, p < 0.01) from 1989 to 2004 and sharply increased (6.92 × 108 m3·yr−1, R2 = 0.92, p < 0.01) after 2004. The relationship between the lake and climate over the last 30 years implies that the decreasing evaporation and increasing precipitation were the major factors affecting the fluctuation of lake storage. Meanwhile, the temporal heterogeneity of the driving mechanism of climate change led to the phased characteristics of lake storage change. In detail, obvious warming led to the shrinkage of the QHL before 2004 through increasing evaporation, while humidifying and accelerating wind stilling dominated the expansion of the QHL after 2004 by increasing precipitation and decreasing evaporation. This paper indicated that the frameworks of multi-source remote sensing and accurate detection of water bodies were required to protect the high-altitude lakes from further climate changes based on the findings of this paper of the QHL recently. The framework presented herein can provide accurate detection and monitoring of water bodies in different locations in the Qinghai-Tibet Plateau, and provide a necessary basis for future political activities and decisions in terms of sustainable water resource management.

In this paper, a ground target extraction system for a novel LiDAR, airborne streak tube imaging LiDAR (ASTIL), is proposed. This system depends on only a single echo and a single data source, and can achieve fast ground target extraction. This system consists of two modules: Autofocus SSD (Single Shot MultiBox Detector) and post-processing. The Autofocus SSD proposed in this paper is used for object detection in the ASTIL echo signal, and its prediction speed exceeds that of the original SSD by a factor of three. In the post-processing module, we describe in detail how the echoes are processed into point clouds. The system was tested on a test set, and it can be seen from a visual perspective that satisfactory results were obtained for the extraction of buildings and trees. The system mAPIoU=0.5 is 0.812, and the FPS is greater than 34. The results prove that this ASTIL processing system can achieve fast ground target extraction based on a single echo and a single data source.

Spread-F (SF) is one of the most important types of the ionospheric irregularities as it causes ionospheric scintillation which can severely affect the performance and reliability of communication, navigation, and radar systems. The ionosonde provides the most effective and economical way to study the ionosphere and SF. However, the manual identification of SF from an ionogram is boring and hard work. To automatically identify SF on the ionogram and extend the study of SF into the middle and low latitudes of East Asia, this paper presents a statistical analysis of SF in this region, based on the naïve Bayesian classifier. The results showed that the accuracy of automatic identification reached up to 97% on both the validation datasets and test datasets composed of Mohe, I-Cheon, Jeju, Wuhan, and Sanya ionograms, suggesting that it is a promising way to automatically identify SF on ionograms. Based on the classification results, the statistical analysis shows that SF has a complicated morphology in the middle and low latitudes of East Asia. Specifically, there is a peak of occurrence of SF in the summer in I-Cheon, Jeju, Sanya, and Wuhan; however, the Mohe station has the highest occurrence rate of SF in December. The different seasonal variations of SF might be due to the different geographic local conditions, such as the inland-coastal differences and formation mechanism differences at these latitudes. Moreover, SF occurs more easily in the post-midnight hours when compared with the pre-midnight period in these stations, which is consistent with the previous results. Furthermore, this paper extracts the frequency SF (FSF) index and range SF (RSF) index to characterize the features of SF. The results shows that the most intense FSF/RSF appeared in the height range of 220–300 km/1–7 MHz in these stations, although there are different magnitude extensions on different season in these regions. In particular, strong spread-F (SSF) reached its maximum at the equinox at Sanya, confirming the frequent SSF occurrence at the equinox at the equator and low latitudes. These results would be helpful for understanding the characteristics of SF in East Asia.

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismic data. Artificial neural network have been used to solve nonlinear inverse problems in recent years. This research obtained an acoustic impedance profile by feeding seismic profile and background impedance into a well-trained self-attention U-Net. The U-Net got convergence by appropriate iteration, and the output predicted the impedance profiles in the test. To value the quality of predicted profiles from different perspectives, e.g., correlation, regression, and similarity, we used four kinds of indexes. At the same time, our results were predicted by conventional methods (e.g., deconvolution with recursive inversion, and TV regularization) and a 1D neural network was calculated in contrast. Self-attention U-Net showed to be robust to noise and does not require prior knowledge. Furthermore, spatial continuity is also better than deconvolution, regularization, and 1D deep learning methods in contrast. The U-Net in this paper is a type of full convolutional neural network, so there are no limits to the shape of the input. Based on this, a large impedance profile can be predicted by U-Net, which is trained by a patchy training dataset. In addition, this paper applied the proposed method to the field data obtained by the Ceduna survey without any label. The predictions prove that this well-trained network could be generalized from synthetic data to field data.

From 19 July 2022 to 31 August 2022, a rare persistent drought and heat event occurred in the middle of the Yangtze River basin (MYRB). Normalized difference vegetation Index (NDVI) over 25% of the area decreased more than 0.05 compared with the climatology, causing extremely agricultural drought disaster and economic losses to China. Previous studies have shown that the occurrence of compound drought and heat events (CDHEs) in the MYRB was associated with intra-seasonal oscillations (ISOs) from different latitudes. Nevertheless, what was the role of ISOs at different latitudes in the formation of the CDHE? To address this question, this paper designed a numerical simulation experiment of partial lateral forcing to investigate the changes in meteorological elements by removing the signals of ISOs on different lateral boundaries. We found that the wave series formed in the upper troposphere at 200 hPa played a significant role in the occurrence of the CDHE in the northern part of the MYRB in this progress. It was found that the ISO component of the northern boundary caused the mean temperature to rise by 2.4 °C and aggravated the drought in 53.7% of the region. On the other hand, the anticyclone anomaly in the lower troposphere at 800 hPa had a continuous impact on the southern and eastern boundaries. It was found that the ISO component of these two boundaries can increase the average temperature by 1.93 °C in the MYRB and intensify the drought in 49.7% of the area. In the developing period of the CDHE, the South Asian high and the Western North Pacific subtropical high were coupled with each other and jointly controlled the MYRB, so that the significant positive geopotential height anomaly stayed above the MYRB for a long time, which was conducive to the development of local subsidence. The results of this paper will help to better understand the formation mechanism of CDHEs in the MYRB and assist meteorologists to prevent and forecast the occurrence of CDHEs in advance.

This paper focuses on the problem of joint detection, tracking, and classification (JDTC) for multiple extended objects (EOs) within a Poisson multi-Bernoulli (MB) mixture (PMBM) filter, where an EO is described as an ellipse, and the ellipse is modeled by a random matrix. The EOs are classified according to the size information of the ellipse. Usually, detection, tracking, and classification are processed step-by-step. However, step-by-step processing ignores the coupling relationship between detection, tracking, and classification, resulting in information loss. In fact, detection, tracking, and classification affect each other, and JDTC is expected to be beneficial for achieving better overall performance. In the multi-target tracking problem based on RFS, the overall performance of the PMBM filter satisfying the conjugate priors has been verified to be superior to other filters. Specifically, the PMBM filter propagates multiple MB simultaneously during iterative updates and model the distribution of hitherto undetected EOs. At present, the PMBM filter is only applied to multiple extended objects tracking problem. Therefore, we consider using the PMBM filter to solve the JDTC problem of multiple EOs and further improve JDTC performance. Furthermore, the closed-form implementation based on the product of a gamma Gaussian inverse Wishart (GGIW) and class probability mass function (PMF) is proposed. The details of parameters calculation in the implementation process and the derivation of class PMF are presented in this paper. Simulation experiments verify that the proposed algorithm, named the JDTC-PMBM-GGIW filter, performs well in comparison to the existing JDTC strategies for multiple extended objects.

As the scale of the power grid continues to expand, the human-based inspection method struggles to meet the needs of efficient grid operation and maintenance. Currently, the existing UAV inspection system in the market generally has short endurance power time, high flight operation requirements, low degree of autonomous flight, low accuracy of intelligent identification, slow generation of inspection reports, and other problems. In view of these shortcomings, this paper designs an intelligent inspection system based on self-developed UAVs, including autonomous planning of inspection paths, sliding film control algorithms, mobile inspection schemes and intelligent fault diagnosis. In the first stage, basic data such as latitude, longitude, altitude, and the length of the cross-arms are obtained from the cloud database of the power grid, while the lateral displacement and vertical displacement during the inspection drone operation are calculated, and the inspection flight path is generated independently according to the inspection type. In the second stage, in order to make the UAV’s flight more stable, the reference-model-based sliding mode control algorithm is introduced to improve the control performance. Meanwhile, during flight, the intelligent UAV uploads the captured photos to the cloud in real time. In the third stage, a mobile inspection program is designed in order to improve the inspection efficiency. The transfer of equipment is realized in the process of UAV inspection. Finally, to improve the detection accuracy, a high-precision object detector is designed based on the YOLOX network model, and the improved model increased the mAP0.5:0.95 metric by 2.22 percentage points compared to the original YOLOX_m for bird’s nest detection. After a large number of flight verifications, the inspection system designed in this paper greatly improves the efficiency of power inspection, shortens the inspection cycle, reduces the investment cost of inspection manpower and material resources, and successfully fuses the object detection algorithm in the field of high-voltage power transmission lines inspection.

Landmines and explosive remnants of war are a significant threat in tens of countries and other territories, causing the deaths or injuries of thousands of people every year, even long after military conflicts. Effective technical means of remote detecting, localizing, imaging, and identifying mines and other buried explosives are still sought and have a great potential utility. This paper considers a positioning system used as a supporting tool for a handheld ground penetrating radar. Accurate knowledge of the radar antenna position during terrain scanning is necessary to properly localize and visualize the shape of buried objects, which helps in their remote classification and makes demining safer. The positioning system proposed in this paper uses ultrawideband radios to measure the distances between stationary beacons and mobile units. The measurements are processed with an extended Kalman filter based on an innovative dynamics model, derived from the model of a pendulum motion. The results of simulations included in the paper prove that using the proposed pendulum dynamics model ensures a better accuracy than the accuracy obtainable with other typically used dynamics models. It is also demonstrated that our positioning system can estimate the radar antenna position with the accuracy of single centimeters which is required for appropriate imaging of buried objects with the ground penetrating radars.

Due to the limitations of the horizontal bounding boxes for locating the oriented ship targets in synthetic aperture radar (SAR) images, the rotated bounding box (RBB) has received wider attention in recent years. First, the existing RBB encodings suffer from boundary discontinuity problems, which interfere with the convergence of the model, and then lead to some problems, such as the inaccurate location of the ship targets in the boundary state. Thus, from the perspective that the long-edge features of the ships are more representative of their orientation, the long-edge decomposition RBB encoding has been proposed in this paper, which can avoid the boundary discontinuity problem. Second, the problem of the positive and negative samples imbalance is serious for the SAR ship images because only a few ship targets exist in the vast background of these images. Since the ship targets of different sizes are subject to varying degrees of interference caused by this problem, a multiscale elliptical Gaussian sample balancing strategy has been proposed in this paper, which can mitigate the impact of this problem by labeling the loss weights of the negative samples within the target foreground area with multiscale elliptical Gaussian kernels. Finally, experiments based on the CenterNet model were implemented on the benchmark SAR image dataset SSDD (SAR ship detection dataset). The experimental results demonstrate that our proposed long-edge decomposition RBB encoding outperforms other conventional RBB encodings in the task of oriented ship detection in SAR images. In addition, our proposed multiscale elliptical Gaussian sample balancing strategy is effective and can improve the model performance.

Obtaining accurate and timely crop area information is crucial for crop yield estimates and food security. Because most existing crop mapping models based on remote sensing data have poor generalizability, they cannot be rapidly deployed for crop identification tasks in different regions. Based on a priori knowledge of phenology, we designed an off-center Bayesian deep learning remote sensing crop classification method that can highlight phenological features, combined with an attention mechanism and residual connectivity. In this paper, we first optimize the input image and input features based on a phenology analysis. Then, a convolutional neural network (CNN), recurrent neural network (RNN), and random forest classifier (RFC) were built based on farm data in northeastern Inner Mongolia and applied to perform comparisons with the method proposed here. Then, classification tests were performed on soybean, maize, and rice from four measurement areas in northeastern China to verify the accuracy of the above methods. To further explore the reliability of the method proposed in this paper, an uncertainty analysis was conducted by Bayesian deep learning to analyze the model’s learning process and model structure for interpretability. Finally, statistical data collected in Suibin County, Heilongjiang Province, over many years, and Shandong Province in 2020 were used as reference data to verify the applicability of the methods. The experimental results show that the classification accuracy of the three crops reached 90.73% overall and the average F1 and IOU were 89.57% and 81.48%, respectively. Furthermore, the proposed method can be directly applied to crop area estimations in different years in other regions based on its good correlation with official statistics.

Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management.

Limited by meteorological conditions, airspace, complex terrain and other factors, airborne millimeter-wave InSAR will inevitably face the situation of no control point layout when acquiring terrain data in the difficult mapping areas in Southwest China, which increases the difficulty of subsequent data processing. Moreover, the layout of control points in difficult mapping areas consumes a lot of manpower and time, which is not suitable for large-scale high-precision topographic mapping. To solve these problems, this paper proposes an automatic extraction of tie-points and interferometric calibration technology based on tie-points. This technology develops the automatic extraction algorithm of tie-points based on SAR + SIFT + RANSAC to obtain evenly distributed tie-points of adjacent images, and uses the evenly distributed tie-points as real known points to recalibrate the interference parameters, then carries out elevation transfer and elevation inversion through the tie-points of overlapping areas, thus realizing high-precision mapping without control points for airborne millimeter-wave InSAR. This paper uses measured data to verify the technology, and compares it with the areas with control points and marking points. The comparison results of elevation accuracy prove the feasibility and effectiveness of this method. This paper also discusses the difficulties in the treatment of typical areas, such as water areas, urban areas and mountain areas, and gives reasonable solutions that have good engineering application value.

Transportation agencies in northern environments spend a considerable amount of their budget on salt for winter operations. For example, in the state of Indiana, there are approximately 140 salt storage facilities distributed throughout the state and the state expends between USD 30 M and USD 60 M on inventory and delivery each year. Historical techniques of relying on visual estimates of salt stockpiles can be inaccurate and do not scale well for managing the supply chain during the winter or planning for re-supply during summer months. This paper describes the implementation of a portable pole mounted LiDAR system that can be used to inventory a large barn in under 15 min and describes how this system has been deployed over 90 times at 30 facilities. A quick and easy accuracy test, based upon conservation of volume, was used to provide an independent check on the system performance by repositioning portions of the salt pile. Those tests indicated stockpile volumes can be estimated with an accuracy of approximately 0.1%. The paper concludes by discussing how this technology can be permanently installed near the roof for systematic monitoring throughout the year.

Evaluation of the spatial distribution of urban emergency shelters can effectively identify defects in the current distribution of urban emergency shelters and weaknesses in the overall evacuation service capacity of the city and provide reference for improving the level of urban emergency shelters and evacuation and disaster relief capacity. At present, evaluation of the spatial distribution of urban emergency shelters is mainly carried out on three aspects: effectiveness, accessibility, and safety. However, there are problems, such as individual evaluation scales and incomplete indicator systems, unreasonable allocation of indicator weights, and ignoring the influence of fuzzy incompatibility between different indicator attributes on the evaluation results. In this paper, we start from two scales, the individual emergency shelter and the regional groups of emergency shelters. Based on the five criteria of effectiveness, accessibility, safety, suitability, and fairness, the evaluation indicator system of the spatial distribution of urban emergency shelters was constructed. It was combined with AHP, CRITIC, the optimal weight coefficient solution method based on the maximum deviation sum of squares theory, and fuzzy optimization theory to construct a multi-indicator evaluation model. Further, the spatial distribution condition of the existing emergency shelter in Shanghai was evaluated. The results show that: among the existing ninety-one emergency shelters in Shanghai, there are nine places with unreasonable spatial distribution; nineteen places are comparatively unreasonable. From the scale of regional groups, there is one district (Pudong New District) with unreasonable spatial distribution: its relative superiority value is far lower than other districts, and there are three districts that are comparatively unreasonable. Further, the evaluation scores of the spatial distribution reasonableness of emergency shelters in each region of Shanghai show a high–low–middle distribution from the downtown area of Shanghai outward. The evaluation indicator system and evaluation method used in this paper can effectively reflect the deficiencies in the spatial distribution of urban emergency shelters, thus providing a reference for the relevant departments to improve and plan emergency shelters.

Video satellites can stare at target areas on the Earth’s surface to obtain high-temporal-resolution remote sensing videos, which make it possible to track objects in satellite videos. However, it should be noted that the object size in satellite videos is usually small and has less textural property, and the moving objects in satellite videos are easily occluded, which puts forward higher requirements for the tracker. In order to solve the above problems, consider that the remote sensing image contains rich road information, which can be used to constrain the trajectory of the object in a satellite video, this paper proposes an improved Kernel Correlation Filter (KCF) assisted by road information to track small objects, especially when the object is occluded. Specifically, the contributions of this paper are as follows: First, the tracking confidence module is reconstructed, which integrates the peak response and the average peak correlation energy of the response map to more accurately judge whether the object is occluded. Then, an adaptive Kalman filter is designed to adaptively adjust the parameters of the Kalman filter according to the motion state of the object, which improves the robustness of tracking and reduces the tracking drift after the object is occluded. Last but not least, an object tracking strategy assisted by road information is recommended, which searches for objects with road information as constraints, to locate objects more accurately. After the above improvements, compared with the KCF tracker, our method improves the tracking precision by 35.9% and the tracking success rate by 18.1% with the tracking rate at a speed of 300 frames per second, which meets the real-time requirements.

Clusters of dead trees are forest fires-prone. To maintain ecological balance and realize its protection, timely detection of dead trees in forest remote sensing images using existing computer vision methods is of great significance. Remote sensing images captured by Unmanned aerial vehicles (UAVs) typically have several issues, e.g., mixed distribution of adjacent but different tree classes, interference of redundant information, and high differences in scales of dead tree clusters, making the detection of dead tree clusters much more challenging. Therefore, based on the Multipath dense composite network (MDCN), an object detection method called LLAM-MDCNet is proposed in this paper. First, a feature extraction network called Multipath dense composite network is designed. The network’s multipath structure can substantially increase the extraction of underlying and semantic features to enhance its extraction capability for rich-information regions. Following that, in the row, column, and diagonal directions, the Longitude Latitude Attention Mechanism (LLAM) is presented and incorporated into the feature extraction network. The multi-directional LLAM facilitates the suppression of irrelevant and redundant information and improves the representation of high-level semantic feature information. Lastly, an AugFPN is employed for down-sampling, yielding a more comprehensive representation of image features with the combination of low-level texture features and high-level semantic information. Consequently, the network’s detection effect for dead tree cluster targets with high-scale differences is improved. Furthermore, we make the collected high-quality aerial dead tree cluster dataset containing 19,517 images shot by drones publicly available for other researchers to improve the work in this paper. Our proposed method achieved 87.25% mAP with an FPS of 66 on our dataset, demonstrating the effectiveness of the LLAM-MDCNet for detecting dead tree cluster targets in forest remote sensing images.

An urban environment defines a specific micro-climate which directly affects the quality of life in urbanized areas and often has a negative impact on urban populations. Phenomena like urban heat and surface heat islands are direct products of an urban lifestyle. Urban meteorological networks (UMNs) are a tool that can help to better understand and analyze the current situation and make the right decisions about future urban development. Deployed to monitor and record different objects and their states inside urban areas, UMNs build a long-term meteorological data time series database. The most commonly used systems for achieving this goal include wireless sensor networks (WSNs). This paper presents a combined experience in deploying three different WSN systems. During seven years of research in this field, the authors have recognized the importance of data reliability in data acquisition. More importantly, due to the lack of research addressing the reliability of the data received from WSNs by the core segment of the server (processes used in receiving, validating, parsing, and storing data into a database instance), the received data are used in scientific studies without questioning their reliability. To determine the possibility of shifting information provided by the data measured from sensor networks before it is stored in a desired form of database, this paper proposes a highly reliable socket server model. The model is built with high reliability and performance in mind and it includes three major processes, which use a combination of signals and control messages to pass information about their states. A case study is performed using high-end hardware, running a Linux operating system stressed to its limits. Repetition testing revealed inconsistency in the information provided by the operating system to the application layer, which could lead to the loss of information about short-term and rarely occurring monitored objects. The results lead to the conclusion that there is a clear need for a higher level of data reliability in the process of data acquisition by UMNs. The proposed socket server should fill this gap within the server&rsquo, s core segment.

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.

To support the paper with the above title. This paper presents a field study of the HydroColor (HC) and EyeOnWater (EOW) smartphone Apps to evaluate water quality for inland waters in Eastern Australia. The file here contains the raw data upon which the analysis is based. It includes detailed measurements at 32 sites, covering inland waters with a large range in sediment and algal concentrations., Refer to paper (title above) for full details of data collection and analysis.

United Nations Sustainable Development Goal SDG11.3.1—the ratio of land consumption rate (LCR) to population growth rate (PGR) (LCRPGR)—aims to measure the efficiency and sustainability of urban land use. In recent years, SDG11.3.1 has been widely used in sustainable urban development research. However, previous studies have focused on the urban core area, while the sustainable development status of the urban peripheral areas (suburban and rural areas) that contribute significantly to the ecological environment has been neglected. To this end, relying on land use/cover change (LUCC) data obtained from high-resolution remote sensing satellite images rather than the single impervious surface data used in traditional research, according to the multiple functions of the land use type, the city is divided into three types of space: production, living, and ecological spaces. Research from the perspective of multi-scale coordination is of great significance for gaining a comprehensive understanding of the sustainable development status of urban space. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China as an example, in this paper, LUCC remote sensing data and comprehensive population and gross domestic product (GDP) data are used. From the multi-functional production-living-ecological space perspective, based on the original land use efficiency indicator, the ratio of land consumption rate (LCR) to economic growth rate (EGR) (LCREGR) is introduced and the analytic hierarchy process (AHP) is used to comprehensively evaluate the sustainable development level (SDL) of the space between 2000–2010 and 2010–2020 on the urban agglomeration and prefecture-level city scales. The results show that (1) the level of and changes in the spatial sustainable development are significantly different at different scales; (2) the division of the production-living-ecological spaces can guide cities to optimize different types of spaces in the future. This paper proposes a new evaluation method for spatial sustainable development, which provides a useful reference for any country or region in the world.

The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper. This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original U-Net network with a support vector machine, and finally uses the majority voting game theory algorithm to fuse the multifeature classification results and obtain the final classification results. This study used the forest distribution in Xingbin District, Laibin City, Guangxi Zhuang Autonomous Region as the research object, which is based on Landsat 8 multispectral remote sensing images, and, by combining spectral features, spatial features, and advanced semantic features, overcame the influence of the reduction in spatial resolution that occurs with the deepening of the network on the classification results. The experimental results showed that the improved algorithm can improve classification accuracy. Before the improvement, the overall segmentation accuracy and segmentation accuracy of the forestland increased from 90.50% to 92.82% and from 95.66% to 97.16%, respectively. The forest cover results obtained by the algorithm proposed in this paper can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time vegetation growth change models.

Tianwen-1 is the first Mars probe launched by China and the first mission in the world to successfully complete the three steps of exploration (orbiting, landing, and roving) at the one time. Based on the unverifiable descent images which cover the full range of the landing area, trajectory recovery and fine terrain reconstruction are important parts of the planetary exploration process. In this paper, a novel trajectory recovery and terrain reconstruction (TR-TR) algorithm employing descent images is proposed for the dual-restrained conditions: restraints of the flat terrain resulting in an unstable solution of the descent trajectory and of the parabolic descent trajectory causing low accuracy of terrain reconstruction, respectively. A landing simulation experiment on a landing field with Mars-like landform was carried out to test the robustness and feasibility of the algorithm. The experiment result showed that the horizontal error of the recovered trajectory didn’t exceed 0.397 m, and the elevation error of the reconstructed terrain was no more than 0.462 m. The algorithm successfully recovered the descent trajectory and generated high-resolution terrain products using in-orbit data of Tianwen-1, which provided effective support for the mission planning of the Zhurong rover. The analysis of the results indicated that the descent trajectory has parabolic properties. In addition, the reconstructed terrain contains abundant information and the vertical root mean square error (RMSE) of ground control points is smaller than 1.612 m. Terrain accuracy obtained by in-orbit data is lower than that obtained by field experiment. The work in this paper has made important contributions to the surveying and mapping of Tianwen-1 and has great application value.

Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the L2,1 norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method.

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui.

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.

Precipitation is closely related to the production and daily life of human beings, so accurate precipitation measurement is of great significance. Spaceborne synthetic aperture radar (SAR) is a microwave remote sensing technology with high resolution, which provides an opportunity to improve the accuracy of precipitation inversion. In this paper, the radar attenuation expression is analyzed according to the scattering characteristics of rain, snow and ground. Combined with the Volterra integral equation of the second kind, the solution to the expression, the precipitation horizontal variation of the double-layer model, can be obtained. The simulated result of this method is in good agreement with the given horizontal variation of precipitation. Compared with the original VIE method, which only considers the effect of rainfall, the method in this paper considers both rainfall and snowfall; compared with the Model Oriented Statistical (MOS) method, the method in this paper not only reduces the number of empirical coefficients used and thus reduces the workload in the early stage and retrieval process and its application limits, but it will also increase the accuracy of the inversion of the horizontal variation.

Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested., This work is supported by the R&D Project BioDAgro—Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST-Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.

Infrared (IR) target detection is an important technology in the field of remote sensing image application. The methods for IR image target detection are affected by many characteristics, such as poor texture information and low contrast. These characteristics bring great challenges to infrared target detection. To address the above problem, we propose a novel target detection method for IR images target detection in this paper. Our method is improved from two aspects: Firstly, we propose a novel residual thermal infrared network (ResTNet) as the backbone in our method, which is designed to improve the feature extraction ability for low contrast targets by Transformer structure. Secondly, we propose a contrast enhancement loss function (CTEL) that optimizes the weights about the loss value of the low contrast targets’ prediction results to improve the effect of learning low contrast targets and compensate for the gradient of the low-contrast targets in training back propagation. Experiments on FLIR-ADAS dataset and our remote sensing dataset show that our method is far superior to the state-of-the-art ones in detecting low-contrast targets of IR images. The mAP of the proposed method reaches 84% on the FLIR public dataset. This is the best precision in published papers. Compared with the baseline, the performance on low-contrast targets is improved by about 20%. In addition, the proposed method is state-of-the-art on the FLIR dataset and our dataset. The comparative experiments demonstrate that our method has strong robustness and competitiveness.

In this paper, we deal with the problem of retrieving maps of tropospheric Water Vapor (WV) concentration by means of a set of Low Earth Orbit (LEO) satellites orbiting in the same plane and along the same direction. It is assumed that a number of microwave links is deployed between a group of satellites with microwave transmitters onboard and another group with receivers. It is also assumed that the Normalized Differential Spectral Absorption (NDSA) approach is used to provide time series of Integrated Water Vapor (IWV) along each link. The set of links scans an annular region belonging to the orbital plane of the LEO satellites, so that the time series of the IWV measurements can be exploited to retrieve the WV concentration in such a region. This is a typical tomographic inversion problem. The geometry of the acquisition system and the path-integrated nature of measurements well fit the application of the Exterior Reconstruction Tomographic Algorithm (ERTA), which was proposed several decades ago in contexts different from remote sensing. In this paper, we investigate the potential of ERTA for the WV retrieval and compare its performance with that of a least square inversion approach already presented in the literature. The compared analysis has been carried out through simulations that have highlighted the peculiarities and retrieval capabilities of the two tomographic methods, as well as the impact of the richness of the satellite constellation on the overall performance.

Interference multipath is an important factor to affect the anti-jamming performance for the global navigation satellite system (GNSS) antenna array receiver. However, interference multipath must be considered in practical application. In this paper, the antenna array model for interference multipath is analyzed, and an equivalent model for interference multipath is proposed. According to the equivalent interference multipath model, the influence of interference multipath on anti-jamming performance is analyzed from the space only processing (SOP) and space-time adaptive processing (STAP). Interference multipath can cause loss of the degree of freedom (DoF) of SOP. Through analysis of the equivalent model and STAP mechanism, it further reveals how the STAP can solve the interference multipath. The simulation experiments prove that the equivalent model is effective, and the analysis conclusion is correct. This paper also points out that the interference bandwidth is wider and more taps in STAP are required, under the same experiment conditions.

The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m.

The TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) bistatic system provides high-resolution and high-quality interferometric data for global topographic measurement. Since the twin TanDEM-X satellites fly in a close helix formation, they can acquire approximately simultaneous synthetic aperture radar (SAR) images, so that temporal decorrelation and atmospheric delay can be ignored. Consequently, the orbital error becomes the most significant error limiting high-resolution SAR interferometry (InSAR) applications, such as the high-precision digital elevation model (DEM) reconstruction, subway and highway deformation monitoring, landslide monitoring and sub-canopy topography inversion. For rugged mountainous areas, in particular, it is difficult to estimate and correct the orbital phase error in TanDEM-X bistatic InSAR. Based on the rigorous InSAR geometric relationship, the orbital phase error can be attributed to the baseline errors (BEs) after fixing the positions of the master SAR sensor and the targets on the ground surface. For the constraint of the targets at a study scene, the freely released TanDEM-X DEM can be used, due to its consistency with the TanDEM-X bistatic InSAR-measured height. As a result, a parameterized model for the orbital phase error estimation is proposed in this paper. In high-resolution and high-precision TanDEM-X bistatic InSAR processing, due to the limited precision of the navigation systems and the uneven baseline changes caused by the helix formation, the BEs are time-varying in most cases. The parameterized model is thus built and estimated along each range line. To validate the proposed method, two mountainous test sites located in China (i.e., Fuping in Shanxi province and Hetang in Hunan province) were selected. The obtained results show that the orbital phase errors of the bistatic interferograms over the two test sites are well estimated. Compared with the widely applied polynomial model, the residual phase corrected by the proposed method contains little undesirable topography-dependent phase error, and avoids unexpected height errors ranging about from −6 m to 3 m for the Fuping test site and from −10 m to 8 m for the Hetang test site. Furthermore, some fine details, such as ridges and valleys, can be clearly identified after the correction. In addition, the two components of the orbital phase error, i.e., the residual flat-earth phase error and the topographic phase error caused by orbital error, are separated and quantified based on the parameterized expression. These demonstrate that the proposed method can be used to accurately estimate and mitigate the orbital phase error in TanDEM-X bistatic InSAR data, which increases the feasibility of reconstructing high-resolution and high-precision DEM. The rigorous geometric constraint, the refinement of the initial baseline parameters, and the assessment for height errors based on the estimated BEs are investigated in the discussion section of this paper.

This paper presents a new SIMO radar system based on a harmonic radar (HR) stepped frequency continuous wave (SFCW) architecture. Simple tags that can be electronically individually activated and deactivated via a DC control voltage were developed and combined to form an MO array field. This HR operates in the entire 2.45 GHz ISM band for transmitting the illumination signal and receives at twice the stimulus frequency and bandwidth centered around 4.9 GHz. This paper presents the development, the basic theory of a HR system for the characterization of objects placed into the propagation path in-between the radar and the reflectors (similar to a free-space measurement with a network analyzer) as well as first measurements performed by the system. Further detailed measurement series will be made available later on to other researchers to develop AI and machine learning based signal processing routines or synthetic aperture radar algorithms for imaging, object recognition, and feature extraction. For this purpose, the necessary information is published in this paper. It is explained in detail why this SIMO-HR can be an attractive solution augmenting or replacing existing systems for radar measurements in production technology for material under test measurements and as a simplified MIMO system. The novel HR transfer function, which is a basis for researchers and developers for material characterization or imaging algorithms, is introduced and metrologically verified in a well traceable coaxial setup.

The Ecological Protection Redline (EPR) is an innovative measure implemented in China to maintain the structural stability and functional security of the ecosystem. By prohibiting large-scale urban and industrial construction activities, EPR is regarded as the “lifeline” to ensure national ecological security. It is of great practical significance to scientifically evaluate the protection effect of EPR and identify the protection vacancies. However, current research has focused only on the protection effects of the EPR on ecosystem services (ESs), and the protection effect of the EPR on ecological connectivity remains poorly understood. Based on an evaluation of ES importance, the circuit model, and hotspot analysis, this paper identified the ecological security pattern in Guangdong–Hong Kong–Macao Greater Bay Area (GBA), analyzed the role of EPR in maintaining ES and ecological connectivity, and identified protection gaps. The results were as follows: (1) The ecological sources were mainly distributed in mountainous areas of the GBA. The ecological sources and ecological corridors constitute a circular ecological shelter surrounding the urban agglomeration of the GBA. (2) The EPR effectively protected water conservation, soil conservation, and biodiversity maintenance services, but the protection efficiency of carbon sequestration service and ecological connectivity were low. In particularly, EPR failed to continuously protect regional large-scale ecological corridors and some important stepping stones. (3) The protection gaps of carbon sequestration service and ecological connectivity in the study area reached 1099.80 km2 and 2175.77 km2, respectively, mainly distributed in Qingyuan, Yunfu, and Huizhou. In future EPR adjustments, important areas for carbon sequestration service and ecological connectivity maintenance should be included. This study provides a comprehensive understanding of the protection effects of EPR on ecological structure and function, and it has produced significant insights into improvements of the EPR policy. In addition, this paper proposes that the scope of resistance surface should be extended, which would improve the rationality of the ecological corridor simulation.

This paper presents an extensive review of nonlinear response-based radar systems. Nonlinear radars are generally used for clutter suppression purposes. These radars detect the nonlinear response generated by diodes and transistors are used as a tag for target localization. Utilizing the nonlinearity properties of these devices, these radars have been used for purposes including locating humans trapped in earthquakes and avalanches, identifying migratory patterns of animals, examining the flight pattern of bees, and detecting bugs in electronic devices. This paper covers the utilization of these radars in human vital signs monitoring, detecting targets in a clutter-rich environment, etc. State-of-the-art nonlinear radars’ high-level architectures, design challenges, and limitations are discussed here. Recent works and results obtained by the authors are also summarized.

High-quality training samples are essential for accurate land cover classification. Due to the difficulties in collecting a large number of training samples, it is of great significance to collect a high-quality sample dataset with a limited sample size but effective sample distribution. In this paper, we proposed an object-oriented sampling approach by segmenting image blocks expanded from systematically distributed seeds (object-oriented sampling approach) and carried out a rigorous comparison of seven sampling strategies, including random sampling, systematic sampling, stratified sampling (stratified sampling with the strata of land cover classes based on classification product, Latin hypercube sampling, and spatial Latin hypercube sampling), object-oriented sampling, and manual sampling, to explore the impact of training sample distribution on the accuracy of land cover classification when the samples are limited. Five study areas from different climate zones were selected along the China–Mongolia border. Our research identified the proposed object-oriented sampling approach as the first-choice sampling strategy in collecting training samples. This approach improved the diversity and completeness of the training sample set. Stratified sampling with strata defined by the combination of different attributes and stratified sampling with the strata of land cover classes had their limitations, and they performed well in specific situations when we have enough prior knowledge or high-accuracy product. Manual sampling was greatly influenced by the experience of interpreters. All these sampling strategies mentioned above outperformed random sampling and systematic sampling in this study. The results indicate that the sampling strategies of training datasets do have great impacts on the land cover classification accuracies when the sample size is limited. This paper will provide guidance for efficient training sample collection to increase classification accuracies.

The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster shells involving non-crisp numbers, in this paper. This is achieved by a vectorized representation of distance, between two non-crisp numbers like the crisp numbers case. Using the proposed clustering method, named vector fuzzy c-spherical shells (VFCSS), all crisp and non-crisp numbers can be clustered by the FCSS algorithm in a unique structure. Therefore, we can implement FCSS clustering over various types of numbers in a unique structure with only a few alterations in the details used in implementing each case. The relations of VFCSS applied to crisp and non-crisp (containing symbolic-interval, LR-type, TFN-type and TAN-type fuzzy) numbers are presented in this paper. Finally, simulation results are reported for VFCSS applied to synthetic LR-type fuzzy numbers, where the application of the proposed method in real life and in geomorphology science is illustrated by extracting the radii of circular agricultural fields using remotely sensed images and the results show better performance and lower cost computational complexity of the proposed method in comparison to conventional FCSS.

Compared with traditional optical and multispectral remote sensing images, hyperspectral images have hundreds of bands that can provide the possibility of fine classification of the earth’s surface. At the same time, a hyperspectral image is an image that coexists with the spatial and spectral. It has become a hot research topic to combine the spatial spectrum information of the image to classify hyperspectral features. Based on the idea of spatial–spectral classification, this paper proposes a novel hyperspectral image classification method based on a segment forest (SF). Firstly, the first principal component of the image was extracted by the process of principal component analysis (PCA) data dimension reduction, and the data constructed the segment forest after dimension reduction to extract the non-local prior spatial information of the image. Secondly, the images’ initial classification results and probability distribution were obtained using support vector machine (SVM), and the spectral information of the images was extracted. Finally, the segment forest constructed above is used to optimize the initial classification results and obtain the final classification results. In this paper, three domestic and foreign public data sets were selected to verify the segment forest classification. SF effectively improved the classification accuracy of SVM, and the overall accuracy of Salinas was enhanced by 11.16%, WHU-Hi-HongHu by 15.89%, and XiongAn by 19.56%. Then, it was compared with six decision-level improved space spectrum classification methods, including guided filtering (GF), Markov random field (MRF), random walk (RW), minimum spanning tree (MST), MST+, and segment tree (ST). The results show that the segment forest-based hyperspectral image classification improves accuracy and efficiency compared with other algorithms, proving the algorithm’s effectiveness.

The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A transformer, whose self-attention mechanism can supply each pixel with a global feature, makes up for the deficiency of the convolutional neural network. Therefore, a multi-scale adaptive segmentation network model (MSST-Net) based on a Swin Transformer is proposed in this paper. Firstly, a Swin Transformer is used as the backbone to encode the input image. Then, the feature maps of different levels are decoded separately. Thirdly, the convolution is used for fusion, so that the network can automatically learn the weight of the decoding results of each level. Finally, we adjust the channels to obtain the final prediction map by using the convolution with a kernel of 1 × 1. By comparing this with other segmentation network models on a WHU building data set, the evaluation metrics, mIoU, F1-score and accuracy are all improved. The network model proposed in this paper is a multi-scale adaptive network model that pays more attention to the global features for remote sensing segmentation.