Synthetic Aperture Radar Amplitude Research Articles

A framework for automated landslide dating utilizing SAR-Derived Parameters Time-Series, An Enhanced Transformer Model, and Dynamic Thresholding

Determining the timing of landslide occurrence is crucial for establishing an accurate, comprehensive and systematic landslide inventory while assessing the potential for reducing landslide risk. Unfortunately, many existing landslide inventories lack temporal information such as the precise time of landslide events. Optical and Synthetic Aperture Radar (SAR) sensors are the most commonly used remote sensing technologies for landslide detection. Unlike optical sensors, SAR sensors are not affected by cloudy conditions and provide valuable imagery regardless of sunlight availability. Therefore, SAR-derived parameters, i.e., SAR amplitude, interferometric coherence, and polarimetric features (alpha and entropy), offer a higher temporal resolution for detecting landslide occurrence times compared to optical data. Despite the advantages, there is currently no universally accepted automatic method for determining the time of landslide events using SAR data. This is due to the lack of anomaly labels and the high time-series volatility in detecting landslide occurrence times. Despite advances in deep-learning methods for anomaly detection in time-series, only a few of them can address these challenges in our case. In this paper, we propose an unsupervised multivariate transformed-based deep-learning model to automatically and efficiently estimate landslide occurrence times using multivariate SAR-derived parameters time-series analysis. The designed gated relative position can increase robustness and temporal context information, by learning global temporal trends in the time-series. Subsequently, the time-series of the anomaly score derived from the proposed Transformer model is analyzed using an adaptive thresholding strategy to dynamically and automatically mark anomalies related to the landslide occurrence. Our research focuses on collapsed landslides characterized by dramatic changes in ground surface topography, with a particular attention for the need of a prior knowledge about landslide boundaries. We assess the performance of the proposed methodology for several collapsed landslides including the July 21, 2020 Shaziba and 23 July, 2019 Shuicheng landslides in China, March 19, 2019 Takht landslide in Iran, June 15, 2018 Jalgyz-Jangak and May 25, 2018 Kugart landslides in Kyrgyzstan, July 7, 2018 Hitardalur landslide in Iceland, and January 25, 2019 Brumadinho landslide in Brazil. In comparison to commonly used neural networks like the LSTM algorithm, our proposed framework leads to a more accurate estimate for the time of landslide failure using time-series of SAR-derived parameters. Furthermore, our results suggest the great potential of SAR data to narrow the time period detected from optical data when used in conjunction with them.

Tracking tsunami propagation and Island’s collapse after the Hunga Tonga Hunga Ha’apai 2022 volcanic eruption from multi-space observations

The quantity and accuracy of satellite-geodetic measurements have increased over time, revolutionizing the monitoring of tectonic processes. Global Navigation Satellite System (GNSS) and satellite radar signals provide observations beyond ground deformation, including how earthquake and tsunami processes affect variations in the ionosphere. Here, we study the Hunga Tonga Hunga Ha’apai (HTHH) volcanic eruption 2022 and its associated tsunami propagation with the analysis GNSS derived Total Electron Content (TEC), Synthetic Aperture Radar (SAR) Sentinel-1 data, complemented with tide gauge observations. We utilize GNSS sites data within a ~ 5000 km radius from the volcanic eruption for estimating the ionospheric perturbation as Vertical TEC. We give evidence on the detection of acoustic gravity, internal gravity, and atmospheric Lamb waves signatures in the TEC perturbation. In particular, the internal gravity waves that concentrated in the southwest of Tonga, directly correlates with the observed tsunami propagation direction as accounted by the tide gauge measurements. However, the acoustic gravity wave signature in the TEC is dominant in the north direction suggesting a surface deformation, which could be verified using Sentinel-1A SAR amplitude data. The analysis presented herein shows that within 5 h of the volcanic eruption, the central part of the HTHH island landscape disappeared with the biggest explosion. The unprecedented detail resolved by integrating satellite data yields previously unknown details of the deformation of the 2022 HTHH volcano eruption.

Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection

Influenced by the imaging mechanism, the occurrence of interference clutter in synthetic aperture radar (SAR) renders the identification of false alarms using detectors challenging. Polarimetric SAR has the potential to improve ship detection performance owing to its distinctive polarization characteristics. The present study proposes a dualistic cascade convolutional neural network (DCCNN) algorithm driven by polarization characteristics for ship detection with fully PolSAR data. First, the new characterizations of fully PolSAR data—Optimized SPAN (OSPAN) and 6-D polarization vector (P6), were mined and defined based on the polarization coherence matrix to introduce more intact information of targets. Then, a backbone feature extraction network with parallel dualistic cascade architecture, basic geometric feature extraction network (BGFENet), and polarization feature enhancement network (PFENet) was specifically constructed, which provided the comprehensive feature representation of targets via feature fusion. Finally, the classification and regression tasks were accomplished in the fully convolutional detection subnetwork relying on extracted multi-scale fusion feature maps, while focusing on target location regression, leading to a decrease in the cost of detection efficiency caused by redundant PFENet. In addition, the corresponding training strategy according to the special architecture of DCCNN was designed to overcome the problem of labeled fully PolSAR data insufficiency and migrate refined geometric knowledge of targets in the single-polarization SAR amplitude image. Experimental results on the established fully polarized SAR dataset show that DCCNN outperforms the competitive CNN-based target detection methods by at least 6.79% in terms of average precision. Moreover, experimental results on the typical large scenes show that DCCNN is at least 0.88% higher than the well-known conventional methods in terms of F1 score.

MAPPING AREAS IMPACTED BY VOLCANIC FLOWS DURING AN ERUPTION USING SYNTHETIC APERTURE RADAR AND OPTICAL IMAGERY

Abstract. During volcanic disasters, the remoteness of the terrains combined with potentially incapacitated lifelines (e.g., disturbed transportation network) prevent ground-based surveys for timely assessment of damage extents. To that effect, we worked to combine satellite optical and Synthetic Aperture Radar (SAR) data to rapidly delineate the areas impacted by fast-moving volcanic flows during an eruption (e.g., Pyroclastic Density Currents (PDCs), lahars), which can in turn be used to target and organize the response efforts. We used the 2015 eruptions of Volcán de Colima (Mexico) and Volcán Calbuco (Chile) volcanoes to calibrate detection thresholds of different types of volcanic flows, from optical and SAR imagery. Optical imagery is used to calculate temporal changes of Normalized Difference Vegetation Index (NDVI) associated with the presence of erupted materials on the surface. SAR amplitude images are used to detect changes in surface roughness (sigma0) attributed to the emplacement of new volcanic flows. Classification of the respective NDVI and SAR amplitude signal changes for different types of volcanic flows is done using very-high-resolution imagery and ground-based data obtained during field work. Linear rescaling of minimal and maximal threshold signals is used to create probability maps of volcanic flow deposits extent, and then combined into a joint probability map to maximize the accuracy of the deposit extents. We tested our ability to generate volcanic flow extent maps during the April 2021 eruption of Soufrière St Vincent, using this detection method and the calibrated threshold values for PDCs and lahars.

Urban Impervious Surface Extraction Based on Deep Convolutional Networks Using Intensity, Polarimetric Scattering and Interferometric Coherence Information from Sentinel-1 SAR Images

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas.

Quantifying Large‐Scale Surface Change Using SAR Amplitude Images: Crater Morphology Changes During the 2019–2020 Shishaldin Volcano Eruption

AbstractMorphological processes often induce meter‐scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote‐sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross‐section, we estimate meter‐scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high‐quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR‐X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co‐registered TerraSAR‐X high‐resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter‐scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of “changed” areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire’s effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data.

A New Probability Distribution for SAR Image Modeling

This article introduces exponentiated transmuted-inverted beta (ET-IB) distribution, supported by a continuous positive real line, as a synthetic aperture radar (SAR) imagery descriptor. It is an extension of the inverted beta distribution, an important texture model for SAR imagery. The considered distribution extension approach increases the flexibility of the baseline distribution, and is a new probabilistic model useful in SAR image applications. Besides introducing the new model, the maximum likelihood method is discussed for parameter estimation. Numerical experiments are performed to validate the use of the ET-IB distribution as a SAR amplitude image descriptor. Finally, three measured SAR images referring to forest, ocean, and urban regions are considered, and the performance of the proposed distribution is compared to distributions usually considered in this field. The proposed distribution outperforms the competitor models for modeling SAR images in terms of some selected goodness-of-fit measures. The results show that the ET-IB distribution is suitable as a SAR descriptor and can be used to develop image-processing tools in remote sensing applications.

Monitoring the spatial dispersion of an oil slick by enhancing and noise-filtering SAR images using SENTINEL-1 satellite repeat-pass observations

ABSTRACT This article presents a method for monitoring the spatial dispersion of an oil slick and the temporal changes it experiences by enhancing and noise-filtering synthetic aperture radar (SAR) amplitude images. When accidental oil spills occur, spatial dispersion of the initial oil cover and assessing the temporal changes that affect it are important factors for recovery operations. Herein, the method proposed aims to facilitate a better understanding of the potential oil spread and oil-float flow from the source of the spill to adjacent coastal zones. Space-borne wide-swath SAR data is used in this regard, as it covers relatively large areas compared with other ground-based remote-sensing techniques and offer day and night observation under all weather conditions in coastal zones. The backscattered radar signal from a VV polarimetric channel, which showed a higher sensitivity for changes in the sea-surface roughness, was used. Heterogeneous surface-scattering phenomena were observed, and the amplitude image was a prominent factor in the formation of a Bragg surface around the seashore, which facilitated a better understanding of the spatial dispersion of a spilt oil layer and its temporal changes. The proposed method was applied to an oil-slick spread that happened after an oil tanker ran aground on a coral reef near Blue Bay Marine Park, south-east of the Mauritius Island, in July 2020. The method for deriving the separability between the oil and the non-oil sea surface was to render per-pixel classifiers more efficient for both speckle smoothing and texture preservation in satellite images. Furthermore, sea wind speed and direction, derived from SAR amplitude images, were used to assess the limitations and reliability of the proposed method. Based on the results, the proposed method indicated a substantial impact for facilitating a better understanding of the spatial dispersion of oil slicks and the temporal changes that affect them.

Oil Spill Detection Based on Deep Convolutional Neural Networks Using Polarimetric Scattering Information From Sentinel-1 SAR Images

Oil spill accidents can cause severe ecological disasters; hence, the timely and effective detection of oil spills on the marine surface is of great significance. Synthetic aperture radar (SAR) is very suitable for large-scale oil spill monitoring. As a more advanced form of SAR, polarimetric SAR (PolSAR) can provide more scattering information of land objects, which can help to improve the accuracy of oil spill detection. However, the current studies of oil spill detection by SAR data have mainly focused on using SAR intensity or amplitude information, and the phase information and other polarimetric information have not been fully utilized. To solve this problem, using Sentinel-1 dual-polarimetric images as the data source, this article presents an intelligent oil spill detection architecture based on a deep convolutional neural network (DCNN), in which both the amplitude information and phase information are utilized. Furthermore, to improve the feature discrimination capability, the Cloude polarimetric decomposition parameters are also integrated into the proposed model. The results show that the improved DeepLabv3+ model, which takes ResNet-101 as the backbone network and group normalization (GN) as the normalization layer, can achieve superior performance than those traditional methods. Moreover, the model is better able to capture the fine details of oil spill instances and can achieve fine-scale segmentation.

Amplitude SAR Imagery Splicing Localization

Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and research work in the SAR field. The drawback of these opportunities is that such images might be exposed to forgeries and manipulations by malicious users, raising new concerns about their integrity and trustworthiness. Up to now, the multimedia forensics literature has proposed various techniques to localize manipulations in natural photographs, but the integrity assessment of SAR images was never investigated. This task poses new challenges, since SAR images are generated with a processing chain completely different from that of natural photographs. This implies that many forensics methods developed for natural images are not guaranteed to succeed. In this paper, we investigate the problem of amplitude SAR imagery splicing localization. Our goal is to localize regions of an amplitude SAR image that have been copied and pasted from another image, possibly undergoing some kind of editing in the process. To do so, we leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input. Then, we examine this fingerprint to produce a binary tampering mask indicating the pixel region under splicing attack. Results show that our proposed method, tailored to the nature of SAR signals, provides better performances than state-of-the-art forensic tools developed for natural images.

Detecting spatio-temporal urban surface changes using identified temporary coherent scatterers

Synthetic aperture radar (SAR) is able to detect surface changes in urban areas with a short revisit time, showing its capability in disaster assessment and urbanization monitoring. Most presented change detection methods are conducted using couples of SAR amplitude images. However, a prior date of surface change is required to select a feasible image pair. We propose an automatic spatio-temporal change detection method by identifying the temporary coherent scatterers. Based on amplitude time series, x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -test and iterative single pixel change detection are proposed to identify all step-times: the moments of the surface change. Then the parameters, e.g., deformation velocity and relative height, are estimated and corresponding coherent periods are identified by using interferometric phase time series. With identified temporary coherent scatterers, different types of temporal surface changes can be classified using the location of the coherent periods and spatial significant changes are identified combining point density and F values. The main advantage of our method is automatically detecting spatio-temporal surface changes without prior information. Experimental results by the proposed method show that both appearing and disappearing buildings with their step-times are successfully identified and results by ascending and descending SAR images show a good agreement.

Environmental Strain on Beach Environments Retrieved and Monitored by Spaceborne Synthetic Aperture Radar

Environmental effects and climate change are lately representing an increasing strain on coastal areas, whose topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. Continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, and tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relationship with precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content.

Exploiting Sentinel-1 data time-series for crop classification and harvest date detection

ABSTRACT Light source independence and the advantage of being less affected by weather conditions than optical remote sensing, as well as the sensitivity to dielectric properties and targets structure, make Synthetic Aperture Radar (SAR), particularly time-series data, a relevant tool for crop processes monitoring. This study aims to benefit from all the amplitude and phase SAR data to perform both a crop classification and a harvest date detection algorithm, supported by the first one for corn and soybean fields. Study area was located in Buenos Aires province, Argentina. To achieve this goal, time-series of Interferometric Coherence (IC) and backscattering values in vertical transmit and vertical receive (), and vertical transmit and horizontal receive () polarizations were generated from Single Look Complex images acquired from C-band SAR satellites Sentinel-1A and −1B. The crop classification was performed using a Random Forest classifier with an overall accuracy of 97%. For its training, both and time-series along the entire crops life cycle were used. Harvest detection algorithm was accomplished by analysing both the IC and time-series in an individual way for both crops. IC changes could be linked to plant structure characteristics along their life cycle (from seeding to harvesting), surface structure induced by harvest operations and post-harvest crops stubble. Based on the latter, individual criteria for corn and soybean were adopted. Crop depending on the determination of the harvest date detection was supported by the crop classification obtained. Harvest detection accuracy over 80 fields was superior to 93% for both crops. The proposed methodology for harvest detection is focused on the crops structural characteristics along its life cycle and the post-harvest stubble, which could lead to different IC behaviours.

Remote characterization of chloride content in oven-dried concrete specimens by using synthetic aperture radar image models

Synthetic aperture radar (SAR) imaging is a remote sensing technique capable of performing noncontact subsurface inspection of dielectric materials like Portland cement concrete. Such techniques can be applied to field inspection of concrete structures and laboratory material characterization. The objective of this paper is to demonstrate the application of SAR imaging on characterizing chloride content inside oven-dried concrete specimens, using a 10-GHz central frequency SAR imaging sensor. Twelve concrete panel specimens (0.3x0.3x0.05 m3) with a 0.45 water-to-cement ratio were manufactured in six groups of different chloride contents (0%, 2%, 4%, 6%, 8% and 10% of cement weight). Five SAR image-based parameters were developed from each SAR image of concrete specimens for chloride, including integrated SAR amplitude of back reflection Iintb, integrated amplitude Iint, average maximum amplitude I-max, critical contour area Ac, and average Gaussian curvature of critical contour Kavg. A parametric analysis on the combined use of different numbers of SAR image parameters was carried out to determine the optimal application of SAR image parameters on the chloride detection problem in this paper. From our result, it is found that, for the purpose of chloride detection inside concrete specimens using single SAR parameter, integrated amplitude Iint shows the best performance. When using two SAR image parameters, combination of integrated amplitude Iint and critical contour area Ac shows the best performance among other two-parameter combinations. When using three SAR image parameters, combination of integrated amplitude Iint, critical contour area Ac, and average maximum amplitude I-max shows the best performance among other three-parameter combinations. Furthermore, it is interesting to report that combination of three SAR image parameters (Iint, Ac, and I-max) provides the overall best performance among all other combinations for the chloride detection problem in oven-dried concrete specimens.

A joint change detection method on complex-valued polarimetric synthetic aperture radar images based on feature fusion and similarity learning

ABSTRACT Existing deep learning-based change detection methods in the field of polarimetric synthetic aperture radar (POLSAR) usually directly deal with intensity images. Methods can be easily transferred from optical image processing to synthetic aperture radar (SAR) image processing. However, the polarization information, which is critical in POLSAR data, has been discarded under normal situations. This paper introduces a novel joint change detection network based on similarity learning for coregistered complex-valued SAR data. A pseudo-siamese network takes both amplitude information and polarization information of POLSAR data as the input. The fusion of low-level, middle-level and high-level features enables the network to keep high-resolution and have strong representation ability during training procedures. Our novel sub-networks, which we term C2-Net and Intensity-Net, deal with the covariance matrix of complex SAR data and amplitude SAR data, respectively. The Intensity-Net works as a typical classification network and detects targets directly. The C2-Net attempts to find the relationship between two SAR data patches. An improved cosine similarity function is used to measure the similarity between two generated feature vectors in C2-Net. Output probability vectors of the two sub-networks are combined for final change detection. The two sub-networks are trained jointly and simultaneously. Control experiments show that proposed improvements are working. Experimental results on dual-polarization complex-valued SAR data from Sentinel-1 demonstrate the effectiveness of the proposed method.

Multi-Scale LG-Mod Analysis for a More Reliable SAR Sea Surface Wind Directions Retrieval

An improved version of the Local-Gradient-Modified (LG-Mod) algorithm for Sea Surface Wind (SSW) directions retrieval by means of Synthetic Aperture Radar (SAR) images is presented. A “local” multi-scale analysis of wind-aligned SAR patterns is introduced to improve the LG-Mod sensitivity to SAR backscattering modulations, occurring locally with various spatial wavelengths. The Marginal Error parameter is redefined, and the adoption of the Directional Accuracy Maximization Criterion (DAMC) allows for the novel Multi-Scale (MS) LG-Mod to automatically select the local processing scale that may be regarded as optimal for pattern enhancement, once a discrete set of scales has been already fixed. Hence, this optimal scale successfully gives evidence to guarantee the best achievable local direction estimation. The assessment of the MS LG-Mod is carried on both simulated SAR images and a Sentinel-1 (S-1) dataset, consisting of 350 Interferometric Wide Swath Ground Range Multi-Look Detected High-Resolution images, which cover the region of the Gulf of Maine. In the latter case, the removal of artifacts and non-wind features from SAR amplitudes is mandatory before directional estimations. In situ wind observations gathered by the National Oceanic and Atmospheric Administration National Data Buoy Center (NOAA NDBC) are exploited for validation. The findings obtained from S-1 data confirm the ones from simulated patterns. The MS LG-Mod analysis performs better than each single-scale one in terms of both percentages of reliable directions and directional Root Mean Square Error (RMSE) values achieved.

A Man-Made Target Extraction Method Based on Scattering Characteristics Using Multiaspect SAR Data

Multiaspect synthetic aperture radar (SAR) can obtain the response of the target to the radar illumination in the angle dimension. The radar cross section data referring to scattering behavior can be obtained by multiaspect SAR. The anisotropy characteristics of the man-made target in the imaging scene can reflect its scattering changing behavior. Meanwhile, the amplitude characteristics of man-made target in some observing angles is target's another scattering property. Based on man-made target's anisotropy characteristics and amplitude characteristics, a dual-channel model (DCM) based on distribution and membership for man-made target extraction is generated in this article. The model considers man-made target's data changing in different observing aspects and strong points in specific aspects. First, the preprocessing of multiaspect data using low-rank matrix decomposition is discussed. Then, the parameters of SAR amplitude images’ distribution function are calculated by using expectation maximization method. Third, one channel is fuzzy C-means (FCM) method combining spatial neighborhood information for amplitude characteristics extraction. Another channel is statistical distribution model for anisotropy characteristics extraction. Finally, The calculated membership degree (MD) and statistical probability describe the man-made target and natural target. C-band circular SAR data is used to validate our method. The result of our DCM is compared with the result of only using a single channel model. The man-made targets are extracted better using the DCM.

Map and Quantify the Ground Deformation Around Salt Lake in Hoh Xil, Qinghai-Tibet Plateau Using Time-Series InSAR From 2006 to 2018

After the bursting of the Zhuonai Lake in Hoh Xil, Qinghai-Tibet Plateau, in 2011, the water area and level of the Salt Lake increased rapidly. The lake area expanding would accelerate the melting of permafrost and contribute to many severe environmental issues, including the surface deformation around the lake. In this article, we retrieve the deformation of the permafrost area around the Salt lake using the time-series synthetic aperture radar (SAR) interferometry (InSAR) method with ENVISAT and Sentinel-1A images from October 2006 to December 2018. In order to get more measurement points, the distributed scatterers were utilized. Moreover, a deformation model combining two components, long-term linear deformation related with temperature, was adopted in the InSAR processing. The experimental results show that before the outburst of the Zhuonai Lake, the ground deformation was not obvious with the mean deformation rate of -5 to 5 mm/year from the ENVISAT results. From 2014 to 2018, the ground deformation around the Salt Lake gradually increased with the maximum deformation rate over -25 mm/year. The water area and the water level of Salt Lake were extracted based on the SAR amplitude images and Cryosat-2 radar altimeter data, showing a rapid increasing trend after 2017. The time-series displacement in the south of lake shows a high correlation with the lake level history. This study clearly illustrate that time-series InSAR could provide valuable information for monitoring potential hazards in permafrost region.

A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling

In this paper, we present a novel statistical model, $\textit{the generalized-Gaussian-Rician}$ (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterizing SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed statistical model is based on the Rician distribution to model the amplitude of a complex SAR signal, the in-phase and quadrature components of which are assumed to be generalized-Gaussian distributed. The proposed amplitude GG-Rician model is further extended to cover the intensity SAR signals. In the experimental analysis, the GG-Rician model is investigated for amplitude and intensity SAR images of various frequency bands and scenes in comparison to state-of-the-art statistical models that include $\mathcal{K}$, Weibull, Gamma, and Lognormal. In order to decide on the most suitable model, statistical significance analysis via Kullback-Leibler divergence and Kolmogorov-Smirnov statistics are performed. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes and its applicability on both amplitude and intensity SAR images. The Matlab package is available at https://github.com/oktaykarakus/GG-Rician-SAR-Image-Modelling.