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Due to the high mobility and strong concealment characteristics of synthetic aperture radar (SAR) targets, the corresponding SAR datasets exhibit few-shot data properties, and there is a significant lack of research on few-shot target detection methods in the SAR domain. Furthermore, this study is subject to the following limitations: the scarcity of SAR data and significant sample variations make it difficult to control class centers using existing methods, and the learned models tend to be biased towards base classes while easily confusing novel classes with base classes. These limitations hinder the generalization of knowledge from base classes when detecting novel class targets. In this work, we propose a novel few-shot SAR target detection method based on Gaussian meta-feature balanced aggregation (GMFBA), which is based on meta-learning. Specifically, we first propose two novel feature aggregation methods with Gaussian metrics, namely Gaussian projection distribution metric (GPDM) and Gaussian kernel mean embedding metric (GKMEM). By estimating class distribution with variational autoencoders to replace traditional class prototypes, we sample from robust distributions and measure projection Wasserstein distance and Gaussian kernel mean embedding distance with prior distributions, obtaining the best robust support features under the optimal measurement results. Then, based on GPDM and GKMEM, we propose a novel balanced inter-class uncorrelated aggregation (BICUA) method, which extracts support features of each class according to the proportion of samples and aggregates them with query features in a balanced manner, promoting feature representation between different classes and ensuring no interference between features to significantly reduce confusion between base classes and novel classes. Specifically, GMFBA outperforms the state-of-the-art method G-FSOD significantly in all settings, achieving state-of-the-art performance. In contrast, the novel class detection performance of GMFBA has shown an average improvement of 8.56% on split1 and split2 of the SRSDD-v1.0 dataset, and an average improvement of 1.41% on split1 and split2 of the MSAR-1.0 dataset. The code is available at https://github.com/Caltech-Z/GMFBA. [ABSTRACT FROM AUTHOR]

Deep learning-based methods have demonstrated exceptional performance in the field of synthetic aperture radar automatic target recognition (SAR ATR). However, obtaining a sufficient number of labeled SAR images remains a significant challenge that can negatively affect the performance of these methods. This is because most deep learning models use the entire target image as input. However, but research has shown that with limited training data, the model may not be able to capture discriminative regions of the image. Instead, they might focus on more useless even harmful image regions for recognition, leading to poor recognition results. In this study, we propose a novel SAR ATR framework that addresses the limitation of limited training data. The proposed framework primarily comprises a global assisted branch, locally enhanced branch, feature capture module, and feature discrimination module. The global-assisted branch conducts an initial recognition and provides a loss based on the entire SAR image during each training epoch and the feature capture module automatically segments and captures crucial image regions for current recognition, which we refer to as the "golden key" of the image. The local enhanced branch then performs another recognition and provides another loss function based on the image parts. Instead of updating the model with two basic recognition losses to roughly search for and capture crucial image parts, a feature discrimination module is proposed to combine the global and local branches in a subtle manner to improve local feature separability or compactness for similar inter-class or dissimilar inner-class sample pairs in global features. This adaptively forced the model to capture more crucial image parts and extract more effective features. Experimental results and comparisons of the MSTAR and OpenSARship datasets indicated that the proposed method achieves superior recognition performance compared to existing methods. The effectiveness of our method was further demonstrated through the visualization of the golden key of the testing images and the recognition performance in ablation experiments. We will release our codes and more experimental results at https://github.com/cwwangSARATR/SARATR_FeaCapture_Discrimination. [Display omitted] [ABSTRACT FROM AUTHOR]

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

[Display omitted] Precise crop mapping is crucial for guiding agricultural production, forecasting crop yield, and ensuring food security. Integrating optical and synthetic aperture radar (SAR) satellite image time series (SITS) for crop classification is an essential and challenging task in remote sensing. Previously published studies generally employ a dual-branch network to learn optical and SAR features independently, while ignoring the complementarity and correlation between the two modalities. In this article, we propose a novel method to learn optical and SAR features for crop classification through cross-modal contrastive learning. Specifically, we develop an updated dual-branch network with partial weight-sharing of the two branches to reduce model complexity. Furthermore, we enforce the network to map features of different modalities from the same class to nearby locations in a latent space, while samples from distinct classes are far apart, thereby learning discriminative and modality-invariant features. We conducted a comprehensive evaluation of the proposed method on a large-scale crop classification dataset. Experimental results show that our method consistently outperforms traditional supervised learning approaches, no matter the training samples are adequate or not. Our findings demonstrate that unifying the representations of optical and SAR image time series enables the network to learn more competitive features and suppress inference noise. [ABSTRACT FROM AUTHOR]

• The impact of the geometric approximation errors on the estimated height is re-examined in the context of airborne array InSAR. • A hybrid 3D phase unwrapping scheme with regard to the geometric approximation error compensation is proposed. • The proposed method is extensively validated using both simulated data and real airborne array InSAR images. Multi-temporal synthetic aperture radar interferometry (MT-InSAR) is able to detect surface deformation and reconstruct a 3D surface model with high precision but requires a long observation period to accumulate the multi-baseline SAR images. The airborne array InSAR system is able to acquire a stack of multi-baseline SAR images in a single acquisition, which significantly improves the 3D modeling capability. However, processing the images obtained by the low-altitude platform using the conventional model will lead to geometric approximation (GA) errors, such as flattened phase error and reference error, which degrade the precision of the 3D reconstruction. In this paper, we quantitatively analyze the error sources of the array-InSAR interferograms and design a hybrid 3D phase unwrapping approach for 3D reconstruction. A hypothesis test is developed to identify the phase ambiguity by comparing the initial solution of the least-square with that of the beam-forming. Three indicators are proposed to identify the reliable arcs, achieving a reliable phase unwrapping. The L 1 norm approach is adopted to detect the unwrapping errors during the spatial unwrapping and a two-tie network strategy is used to process the data in the individual blocks from the perspective of global optimization. Furthermore, an iterative scheme is recommended to compensate the geometric approximation errors. The main advantage of the proposed algorithm is the compensation of the GA error and reliable phase unwrapping. The experimental results by both simulated and real SAR data show that the proposed algorithm can eliminate the GA error and provide a viable solution to rapid 3D SAR imaging with an airborne platform. [ABSTRACT FROM AUTHOR]

Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast's Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is playing an important role in maritime domain awareness. As a fundamental ocean mission, SAR ship detection can offer high-quality services for marine surveillance. It receives widespread concern in the SAR remote sensing community. Recently, deep learning (DL) has greatly improved SAR ship detection accuracy. However, there are still some unperceived imbalance problems that seriously hinder further accuracy improvements among current DL-based SAR ship detectors. Therefore, we propose a novel concept of balance learning (BL) for high-quality SAR ship detection. We first point out the four unperceived imbalance problems, i.e., image sample scene imbalance, positive negative sample imbalance, ship scale feature imbalance, and classification regression task imbalance. Then, we offer some profound insights into these imbalances. Immediately, we propose four effective solutions to handle the above four imbalances correspondingly, i.e., balance scene learning mechanism (BSLM), balance interval sampling mechanism (BISM), balance feature pyramid network (BFPN), and balance classification regression network (BCRN). Finally, combined with the four solutions, a novel balance learning network (BL-Net) is proposed. Ablation studies can confirm each solution's effectiveness. Experimental results on five open datasets (SSDD, Gaofen-SSDD, Sentinel-SSDD, SAR-Ship-Dataset, and HRSID) reveal BL-Net's state-of-the-art SAR ship detection performance compared to the other 30 DL-based SAR ship detectors. Specifically, in contrast to the current most competitive method, the accuracy increase of BL-Net is 2.98% on SSDD, 1.97% on Gaofen-SSDD, 1.49% on Sentinel-SSDD, 0.55% on SAR-Ship-Dataset, and 4.95% on HRSID. Last but not least, the satisfactory ship detection results on another two large-scene Sentinal-1 SAR images confirm BL-Net's strong migration capability. This indicates BL-Net's potential value in marine surveillance. [ABSTRACT FROM AUTHOR]

Ship detection from Synthetic Aperture Radar (SAR) imagery is attracting increasing attention due to its great value in ocean. However, existing most studies are frequently improving detection accuracy at the expense of detection speed. Thus, to solve this problem, this paper proposes HyperLi-Net for high-accurate and high-speed SAR ship detection. We propose five external modules to achieve high-accuracy, i.e., Multi-Receptive-Field Module (MRF-Module), Dilated Convolution Module (DC-Module), Channel and Spatial Attention Module (CSA-Module), Feature Fusion Module (FF-Module) and Feature Pyramid Module (FP-Module). We also adopt five internal mechanisms to achieve high-speed, i.e., Region-Free Model (RF-Model), Small Kernel (S-Kernel), Narrow Channel (N-Channel), Separable Convolution (Separa-Conv) and Batch Normalization Fusion (BN-Fusion). Experimental results on the SAR Ship Detection Dataset (SSDD), Gaofen-SSDD and Sentinel-SSDD show that HyperLi-Net's accuracy and speed are both superior to the other nine state-of-the-art methods. Moreover, the satisfactory detection results on two Sentinel-1 SAR images can reveal HyperLi-Net's good migration capability. HyperLi-Net is build from scratch with fewer parameters, lower computation costs and lighter model that can be efficiently trained on CPUs and is helpful for future hardware transplantation, e.g. FPGAs, DSPs, etc. [ABSTRACT FROM AUTHOR]

As one of the key steps in synthetic aperture radar (SAR) target classification, feature extraction plays an important role. In this study, the multi-level dominant scattering images (DSIs) are generated based on the original images, which can better convey the discrimination capability. DSI is generated by selecting a certain number of dominant scattering points in the original image, which describes the relative positions and amplitudes of the dominant scattering centers. In addition, the background noises, which are usually with low intensities, can be effectively eliminated with high efficiency. For a test sample, each of multi-level DSIs is classified by sparse representation classification (SRC) at first and a reliability level is calculated based on the reconstruction errors, which reflects its contribution to the correct recognition of the present sample. A prescreening of the multi-level DSIs is performed based on their reliability levels. Only those DSIs with higher reliability levels than a predefined threshold is used for target recognition. Afterwards, the selected multi-level DSIs are jointly classified by joint sparse representation (JSR). JSR is actually a multi-task learning algorithm, which comprehensively considers the individual tasks as well as their inner correlations. Therefore, it can better make use of the multi-level to improve the target classification performance. The moving and stationary target acquisition and recognition (MSTAR) dataset is used for experimental evaluation. The results illustrate that the proposed method could achieve notably high recognition accuracy over 97.5% under the standard operating condition (SOC). Moreover, the robustness of the proposed method to various extended operating conditions (EOCs), e.g., configuration variants, large depression variation, noise corruption and partial occlusion, is also superior over some state-of-the-art SAR target classification methods by comparison. [ABSTRACT FROM AUTHOR]

Lake ice provides important social and economic services to local communities, in addition to being a sensitive indicator of climate change. The reduction of ground observations of freshwater ice has led to an increased reliance on the use of satellite remote sensing data. There is currently interest in the retrieval of lake ice properties (e.g., ice thickness, bubble radius, roughness) using synthetic aperture radar (SAR). Roughness at the ice-water interface is particularly important as it has been identified as the dominant mechanism for increasing SAR backscatter throughout the ice season and must be considered in numerical radiative transfer models. Therefore, this study determines optimal ice-water interface roughness height for two subarctic lakes in northern Canada and models backscatter throughout two ice seasons using the snow microwave radiative transfer (SMRT) model. The two lakes for this study are Noell Lake and Malcolm Ramsay Lake. Field observations of ice thickness, snow depth, snow density, and the Canadian Lake Ice Model (CLIMo) are used to parameterize SMRT. Modelled L, C, and X-band backscatter at different incidence angles is assessed using SAR imagery from multiple satellite missions. Root mean square errors ranged from 0.38 to 1.45 dB for Noell Lake and 0.70 to 2.33 dB for Malcolm Ramsay Lake. Discrepancies between modelled and observed backscatter were found to be connected to the representation of roughness at different interfaces within the ice column and changes that occurred during freeze-melt events. These results provide insight into how changes in ice properties impact backscatter throughout the ice season. SMRT is valuable for modelling backscatter from lake ice during the cold season and could be used to develop retrieval algorithms for estimating ice-water interface roughness. This would allow for the development of other inversion models for retrieval of surface ice conditions and ice thickness. • First application of SMRT for modelling backscatter over a complete ice season. • Multi-frequency backscatter was successfully modelled for two Subarctic lakes. • Variable root mean square height was an improvement over a constant value. • Changes in the ice surface and snowpack result in discrepancies in modelling. • SMRT can be useful for future retrieval of crucial lake ice properties. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is indispensable electronic equipment in modern life, which has been widely used in the field of geological exploration, disaster monitoring, and environmental observation. With the increasing complexity of earth observation tasks, a higher receiving resolution of SAR is required, which needs the area of the radar antenna to be increased. However, there is a contradiction between the small launch space and the large working area. The deployable antenna mechanism is expected to meet the challenge. In this paper, a graphical type synthesis method is proposed to carry out the mechanism design of deployable mechanisms. According to the structural analysis and design requirements, the articulated form is adopted to carry sub-planes of planar antennas and provide one kinematic chain of the deployable mechanism. Then, different designed kinematic chains are synthesized to construct novel closed-loop deployable mechanisms. Mobility verification of a selected closed-loop deployable mechanism is carried out by screw theory to demonstrate the validity of type synthesis results. By sharing the provided kinematic chain and symmetrical design of two deployable mechanisms, deployable units with the driving unit are proposed as basic modules of support trusses for planar antennas. As a result, three novel deployable support trusses integrated by a series of deployable units and assembly mechanisms are proposed as the platforms for planar antennas. The deployable mechanisms and integrated support trusses proposed in this paper provide candidates for planar antennas of SAR. [ABSTRACT FROM AUTHOR]

• An adaptive WBI detection method based on TF kurtosis is proposed. Aiming to solving the poor robustness of the existing interference detection methods, this paper introduces TF kurtosis to measure the non-Gaussianity of the echo in the TF domain. It also achieves adaptive statistical detection of RFI with the Neiman Pearson criterion. • The proposed scheme addresses the conventional assumption of a complex Gaussian distribution for SAR echo signals by introducing the Laplace distribution to describe the SAR echo signal without interference, and thereby provide a L 1 –LRMF model, which can bring about a more satisfactory mitigation performance. • The hierarchical Bayesian posterior model promotes the robustness and generalization ability of the WBI mitigation algorithm with respect to unobserved SAR data. The L 1 norm LRMF method aims to describe the observed SAR data specifically, but it has the potential overfitting problem due to the deterministic model. The proposed algorithm utilizes the probability to introduce the uncertainty of interference mitigation model, which promotes the robustness on the unobserved SAR data. • The adoption of VBI for estimating these parameters in the present scheme addresses the lack of adaptability to changes in signal characteristics commonly encountered in existing interference mitigation methods. Moreover, estimating parameters using VBI also addresses the problems associated with parameter estimation based on the usual expectation-maximization algorithm, which involves a multiple-integration process that makes the estimation process extremely complicated and intractable for complex models. Wideband interference (WBI) seriously detracts from synthetic aperture radar (SAR) imaging quality and hinders the subsequent interpretation of images. Meanwhile, the large bandwidth and complex modulation of WBI necessitates the development of mitigation algorithms with strong adaptability and robustness that are lacking in existing algorithms based on filtering and model analysis. The present work addresses this issue by proposing a WBI mitigation algorithm based on variational Bayesian inference (VBI). Firstly, the WBI-contaminated echo signal is identified in the time–frequency (TF) domain through an adaptive statistical detection method. Then, a low-rank matrix factorization is formulated according to the low-rank characteristics of the WBI and the Laplace distribution assumption of the target echo signal in the TF domain. Finally, the WBI component is accurately reconstructed using a mean-field VBI method, and then eliminated from the original SAR echo signal to recover the target echo signal. The effectiveness and robustness of the proposed WBI mitigation algorithm are demonstrated based on its application to simulated and measured SAR data. [ABSTRACT FROM AUTHOR]

• A SVR model was proposed to provide accurate moisture estimation for saline soil. • It does not require prior information like soil surface roughness and soil salinity. • The new SVR model outperformed RFR and MLR in soil moisture estimation. • Sentinel-1A provides a SAR data source to timely map coastal saline soil moisture. Accurate and timely information on soil moisture (SM) is crucial for better understanding the hydrological and ecological processes in humid saline regions. This study investigated the potential of using Sentinel-1A Synthetic Aperture Radar (SAR) imagery and Machine Learning Algorithms for SM mapping in China's east coast. This study used the recursive feature elimination (RFE) technique to select optimum SAR indices from the backscattering coefficients of the sampling date as well as the averaged backscattering coefficients of the neighbouring dates. The newly developed support vector regression (SVR) model provided more accurate SM estimation than that from the random forest regression (RFR) and multivariate linear regression (MLR) models. By using the leave-one-out cross-validation (LOOCV), the achieved coefficient of determination (R2), root mean square error (RMSE), and the relative RMSE (RRMSE) in SM estimation were 0.77, 1.82%, and 10.29%, respectively. The SVR model was able to map the SM effectively over a large area under the condition that the information of soil surface roughness, soil salinity, and land cover types was unknown. Results from this study demonstrate that high-temporal-resolution Sentinel-1A SAR, with its free-accessibility, offers a great source for the SVR model to provide frequent and accurate SM mapping over coastal saline regions. [ABSTRACT FROM AUTHOR]

• The prior cross-track velocity (CTV) estimation is more accurate and significantly simplifies and improves the subsequent extraction, imaging, and localization of moving target. • The accurate clutter suppression method better preserves the moving target envelope and achieves higher signal-to-clutter-and-noise ratio (SCNR). • The improved moving target focusing and localization method which avoids cumbersome CTV search and simplifies the range walk correction (RWC) procedures, has lower computational complexity. • The proposed scheme has higher location accuracy of moving target, achieving accurate estimation and compensation of moving target's 2-D velocities. This paper investigates a novel ground moving target indication (GMTI) scheme for a hypersonic vehicle-borne multichannel synthetic aperture radar (MC-SAR) system with Doppler ambiguity. First, after the range compression and azimuth chirp Fourier transform (CFT), by applying the eigenspace-based adaptive sum and difference beams (EASDB) on the coarse-focused images, the high-precision cross-track velocity (CTV) can be retrieved. Then, utilizing the high-precision CTV to construct an accurate moving target steering vector to extract the moving target energy. With the proposed CFT-two-step method, we have avoided cumbersome CTV search, and simplified the process of range walk correction, resulting in lower computational complexity. Compared to the CFT-center and CFT-two-step methods, the proposed scheme obtains the accurate two-dimension motion parameters of a moving target, and has better performance and robustness in the detection, focusing, and location, preserving the moving target envelope and achieving higher signal-to-clutter-and-noise ratio (SCNR). Finally, using the processing results of simulation and real measured MC-SAR data, we verify the effectiveness and feasibility of our proposed scheme. [ABSTRACT FROM AUTHOR]

Paddy rice is one of the most important staple foods in the world, feeding over 50% of the global population. A quick and accurate mapping of the extent of paddy rice is of critical importance for ensuring food security, studying climate change and monitoring water resources. Based on Sentinel-1A data and rice flooding features, we proposed a rice mapping method called the Automated Rice Mapping using Synthetic Aperture Radar Flooding Signals (ARM-SARFS), in which the key "V" shaped feature in the Sentinel-1A VH backscatter time series rising from the flooding before and after rice transplanting was used for rice mapping. The ARM-SARFS was validated at three study sites in Hubei, Liaoning and Guangdong provinces in China under different rice cropping systems and different geographical and climate conditions. The results showed that even without any training samples, the ARM-SARFS was able to provide a satisfying classification result with an overall accuracy of over 86% and an F 1 score of over 0.85 at all three study sites. With the aid of training samples, the classification performance increased further. When compared with the previously proposed Sentinel-1-based rice mapping methods, the ARM-SARFS improved the overall accuracy by 13.3–37.2%, and the most significant improvement was in the producer's accuracy. The sensitivity test showed that the ARM-SARFS is not sensitive to thresholding, and a high classification accuracy can be achieved at thresholds ranging from −0.025 to 0. These results demonstrated the robustness of ARM-SARFS for automated rice mapping with a high accuracy at large scales. • We proposed an automated rice mapping method (ARM-SARFS) based on Sentinel-1A data. • ARM-SARFS uses the "V" shaped rice flooding signals in VH backscatters time series. • ARM-SARFS is robust for different rice cropping systems. • ARM-SARFS showed a high accuracy even with no training samples. • ARM-SARFS improved the accuracy by 13.3–37.2% compared with previous methods. [ABSTRACT FROM AUTHOR]

A method to differentiate open water from sea ice using quad-polarization C-band synthetic aperture radar (SAR) imagery is presented, based on the roughness properties of the sea surface scattering facets and the polarization ratio (PR) between VV and HH polarization backscatter from the SAR images. In quad-polarization SAR images, the normalized radar cross section (NRCS), which is linked to sea surface roughness, can be used to derive the orientation angle shift and its standard deviation, as well as to compute the measured polarization ratio, PR. An X-Bragg backscatter model is explored to calculate a theoretical look-up table for the polarization ratio as a function of incidence angle, dielectric constant, orientation angle shift and its standard deviation. Thus, ocean surface backscatter can be identified as open water or sea ice by comparing the measured PR with a theoretical model for PR. The proposed method is validated by comparison to collocated optical video images and manual visual interpretations. Estimates of the size, shape and locations of open water from our method are shown to be in good agreement with observed data. The overall accuracy of our algorithm is about 96%. The classification accuracy for sea ice and open water is about 96% and 94%, respectively. • An algorithm for identification of sea ice and open water is proposed. • The SAR polarization ratio is affected by roughness of the sea surface. • Changes in open water and sea ice areas are observed by SAR and helicopter video images. [ABSTRACT FROM AUTHOR]

A prompt and overall understanding of building damages following a disaster is critical, as they are closely related to casualties. As an important active remote sensing technology, synthetic aperture radar can be a valuable tool for assessing building damages in disasters, owing to its large coverage, quick response, non-contact, and independence of weather and light capabilities. Various approaches have been proposed for SAR-based building damage assessment with the development of radar technology and interpretation techniques. While providing numerous choices, these multifarious approaches also make it burdensome to ponder the applicable approach in a specific case, and to reflect on potential fields for further research. Accordingly, this paper hierarchically classified and summarized these approaches to provide a structured understanding of the research status in this field, for assisting in approach decisions and promising field considerations. First, depending on the pre-event data availability, the numerous approaches were classified into change detections employing both pre- and post-event data, and assessments applying only post-event data. Then, determined by the data resolution level and acquisition mode, the plentiful change detection approaches were further distinguished into block-unit approaches analyzing intensity, coherence, or polarimetry features, and building-unit approaches that simply generalize the block-unit approaches, or that concretely explore the detailed individual building features. The post-event data-based assessments, with relatively fewer approaches, were further introduced as methods exploring polarimetry or/and texture features. In each classification category, the principle was first introduced to explain the basic concept and essence of the approaches. An approach review was then provided by organizing relevant studies in a logical or structured way, to facilitate a clear understanding of the overall research status. Favorable parameters in each category were also summarized for easy reference and application in the future. • Various SAR-based building damage assessment methods were hierarchically classified. • Principles of each classified category were introduced to explain the method essence. • Studies in each category were logically organized to provide a clear understanding. • Favorable parameters in each category were summarized to facilitate future reference. • Several future efforts for SAR-based building damage assessment were noted. [ABSTRACT FROM AUTHOR]