Synthetic Aperture Radar Amplitude Images Research Articles

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.

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.

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.

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.

Adjacent-Track InSAR Processing for Large-Scale Land Subsidence Monitoring in the Hebei Plain

Large-scale land subsidence has threatened the safety of the Hebei Plain in China. For tens of thousands of square kilometers of the Hebei Plain, large-scale subsidence monitoring is still one of the most difficult problems to be solved. In this paper, we employed the small baseline subset (SBAS) and NSBAS technique to monitor the land subsidence in the Hebei Plain (45,000 km2). The 166 Sentinel-1A data of adjacent-track 40 and 142 collected from May 2017 to May 2019 were used to generate the average deformation velocity and deformation time-series. A novel data fusion flow for the generation of land subsidence velocity of adjacent-track is presented and tested, named as the fusion of time-series interferometric synthetic aperture radar (TS-InSAR) results of adjacent-track using synthetic aperture radar amplitude images (FTASA). A cross-comparison analysis between the two tracks results and two TS-InSAR results was carried out. In addition, the deformation results were validated by leveling measurements and benchmarks on bedrock results, reaching a precision 9 mm/year. Twenty-six typical subsidence bowls were identified in Handan, Xingtai, Shijiazhuang, Hengshui, Cangzhou, and Baoding. An average annual subsidence velocity over −79 mm/year was observed in Gaoyang County of Baoding City. Through the cause analysis of the typical subsidence bowls, the results showed that the shallow and deep groundwater funnels, three different land use types over the building construction, industrial area, and dense residential area, and faults had high spatial correlation related to land subsidence bowls.

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.

Practical approach for synthetic aperture radar change analysis in urban environments

Change detection using remote sensing imagery is a broad and highly active field of research that has produced many different technical approaches for multiple applications. The majority of these approaches have in common that they do not deliver any detailed information concerning the type, category, or class of the detected changes. With respect to the extraction of such information, recent research often suggests that a land use classification is required. This classification can be accomplished in an unsupervised or supervised way, whereas the practicability of both strategies is more or less limited by the usage of reference or training data. Moreover, expert knowledge is needed to arrive at meaningful land use classes. An approach is presented that overcomes these drawbacks. A time series of synthetic aperture radar amplitude images is considered, enabling the detection of so-called high activity objects in urban environments. Such objects represent the basis of the investigations and denote the input for unsupervised categorization and classification procedures. The method supports even the unexperienced user in learning the actual information content leading to the capability to define a suitable scheme for change classification. Tests carried out on two different datasets suggest that the method is both practical and robust.

Scheme for Characterizing Clutter Statistics in SAR Amplitude Images by Combining Two Parametric Models

Accurate knowledge of the statistical properties of synthetic aperture radar (SAR) amplitude data is essential for the processing and interpretation of SAR images. During the last few decades, parametric models have been extensively investigated for use in characterizing the statistics of SAR amplitude data. Among them, the generalized gamma distribution ( $\text{G}\Gamma \text{D}$ ) has been widely applied in many fields of SAR image processing, as it has been demonstrated to be appropriate for describing the statistical behaviors of both SAR land and sea clutter. However, the $\text{G}\Gamma \text{D}$ is not effective when the third-order sample log-cumulant of SAR data is close to zero as it cannot match the distribution of actual data. In this case, the recently proposed ${\mathcal{ G}}_{\textrm {AO}} $ model shows good performance for fitting the distribution of the actual SAR data. In this paper, we theoretically derive the analytical conditions of the ${\mathcal{ G}}_{\textrm {AO}} $ model applicability. Based on the derivations, a scheme describing the statistical behaviors of SAR pixel amplitudes is given by combining the $\text{G}\Gamma \text{D}$ and the ${\mathcal{ G}}_{\textrm {AO}} $ model. Experiments performed on C-band RADARSAT-2 and L-band ALOS-PALSAR SAR data verify the effectiveness of the proposed scheme. The usefulness of the ${\mathcal{ G}}_{\textrm {AO}} $ model for extremely heterogeneous land clutter, for example, from urban regions, is also demonstrated.

Land consumption monitoring: an innovative method integrating SAR and optical data.

In this paper, the use of synthetic aperture radar (SAR) for the monitoring of land consumption is analyzed. The paper presents an automatic procedure that integrates SAR and optical data, which can be effectively used to generate land consumption maps or update existing maps. The main input of the procedure is a series of SAR amplitude images acquired over a given geographical area and observation period. The main assumption of the procedure is that land consumption is associated with an increase of the SAR amplitude values. Such an increase is detected in the SAR amplitude time series using an automatic Bayesian algorithm. The results based on the SAR amplitude are then filtered using an NDVI map derived from optical imagery. The effectiveness of the proposed procedure is illustrated using SAR data from the Sentinel-1 and TerraSAR-X sensors, and optical data from the Sentinel-2 sensor.

An Automatic Data-Driven Method for SAR Image Segmentation in Sea Surface Analysis

In the context of synthetic aperture radar (SAR) image segmentation, this paper proposes a new automatic unsupervised method addressing sea surface analysis with a focus on oil spill and ship segmentation. Being an evolution of an existing algorithm originally devoted to the detection of a single region of interest, the present method performs a global image segmentation of the whole image. The processing is independent of any model and is driven by data informative content along with intermediate results. Based on graph theory, it makes use of a new defined cost function and assigns cost values to the vertices rather than to the edges of the graph. The experimental results achieved and numerically evaluated on synthetic and real SAR images prove that the method is robust and repeatable, and it does not involve restrictions on image modality acquisition or sensors and does not require radiometric calibration. It can work on amplitude or intensity SAR images, independently on frequency band, polarimetry, and spatial resolution. Qualitative and quantitative performance analyses are carried out along with a comparison with other published works in the same application. Good results are achieved for both oil spill and ship segmentation and robustness by changing seed points position and number. Errors exhibit stable behavior when increasing the number of seed points. Finally, in contrast to most of the existing methods, the proposed technique does not depend on parameters and is generally more robust.

An Improved Multi-Image Matching Method in Stereo-Radargrammetry

The new generation of synthetic aperture radar (SAR) sensors provides us with an opportunity to match multiple high-resolution SAR images. Moreover, the multiple SAR image matching methods have recently gained a lot of attention due to the fact that they can obtain more accurate, better distributed, and more reliable matches than the stereo matching methods. In this letter, we present an improved multi-image matching method to simultaneously identify matches from multiple SAR amplitude images. The proposed method makes better use of the relationships between the pixels in the deformed correlation window and integrates geometric and radiometric information from multiple SAR images. Experiments on Chinese Academy of Surveying and Mapping Synthetic Aperture Radar (CASMSAR) data sets demonstrate that the improved multi-image matching method is capable of providing more accurate and better distributed matches, as well as offering a better multi-image matching solution in stereo-radargrammetry under the conditions of geometric and radiometric distortions, especially in low-texture areas.

A low-cost method applicable worldwide for remotely mapping lava dome growth

Lava dome growth and collapse represents both a significant hazard, as it can trigger pyroclastic density currents, and a monitoring challenge, limiting monitoring to a few known active volcanoes. Here, I propose a new differencing technique based on Synthetic Aperture Radar (SAR) amplitude images to quantify the extent of lava dome growth. This differencing technique, which is both low cost and can be utilized worldwide, is applied to SAR amplitude images at Mount St. Helens and validated using 2004–2008 aerial photography observations. Difference of amplitude images accurately characterize the dome growth location. The low ground resolution of the 2004–2008 SAR data leads to underestimation by 10 to 15% of the dome extent, but the accuracy of this method will increase with the improved resolution of current and future SAR missions. Amplitude images are a low-level SAR product available from all SAR satellites, mostly freely, making the proposed method ideal for systematic, low-cost monitoring of lava dome growth worldwide with minimum processing required.

Radar remote sensing for archaeology in Hangu Frontier Pass in Xin’an, China

As a non-invasive tool, remote sensing can be applied to archaeology taking the advantage of large scale covering, in-time acquisition, high spatial-temporal resolution and etc. In archaeological research, optical approaches have been widely used. However, the capability of Synthetic Aperture Radar (SAR) for archaeological detection has not been fully exploded so far. In this study, we chose Hangu Frontier Pass of Han Dynasty located in Henan Province as the experimental site (included into the cluster of Silk Roads World Heritage sites). An exploratory study to detect the historical remains was conducted. Firstly, TanDEM-X SAR data were applied to generate high resolution DEM of Hangu Frontier Pass; and then the relationship between the pass and derived ridge lines was analyzed. Second, the temporal-averaged amplitude SAR images highlighted archaeological traces owing to the depressed speckle noise. For instance, the processing of 20-scene PALSAR data (spanning from 2007 to 2011) enabled us to detect unknown archaeological features. Finally, the heritage remains detected by SAR data were verified by Ground Penetrating Radar (GPR) prospecting, implying the potential of the space-to-ground radar remote sensing for archaeological applications.

A Comparative Study of Statistical Models for Multilook SAR Images

In this letter, we carry out a comparative study of statistical models for multilook synthetic aperture radar amplitude images. Ten state-of-the-art statistical models are selected for comparison. To achieve a fair evaluation, we estimate all model parameters using the method of log-cumulants and apply the method to an image pyramid with varying pixel spacing (and resolution). The pyramid is created by different image product generation options. In addition to pixel spacing and resolution, we also consider the homogeneity of a scene for performance evaluation and we apply three performance measures. Through this study, it was found out that some models perform well for all resolutions, while the performance of other models depends heavily on the image content.

A Distributed Scatterer Interferometry Approach for Precision Monitoring of Known Surface Deformation Phenomena

This paper presents a new technique for mapping mean deformation velocity in highly decorrelated areas with known deformation patterns, exploiting high-resolution synthetic aperture radar (SAR) data. The implemented method is based on distributed scatterers and first makes use of the Anderson-Darling (AD) statistical test to identify homogenous patches of pixels based on SAR amplitude images. Then, a robust object adaptive parameter estimation is performed to estimate the local gradients of deformation velocity and the local gradients of residual DEM in range and azimuth directions for these patches, utilizing small baseline differential interferograms. Finally, the information obtained from different patches is connected to get the deformation velocity, via a 2-D model-based deformation integration using Bayesian inference. Compared with published multitemporal interferometric work, the main advantage of the newly developed algorithm is that it does not require any phase unwrapping, and because of this, the method is largely insensitive to decorrelation phenomenon occurring in natural terrains and the availability of persistent scatterers (PSs), in contrast to the coherent stacking techniques such as PS interferometry, small baseline subset algorithm, and SqueeSAR. The method is computationally inexpensive with respect to SqueeSAR as only the small baseline interferograms are used for the processing. The method provides spatially dense deformation velocity maps at a suitable object resolution, as compared with a few measured points provided by the stacking techniques in difficult decorrelated regions. High Resolution Spotlight TerraSAR-X data set of Lueneburg in Germany is used as a processing example of this technique.

NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

Speckle noise is an inherent problem in coherent imaging systems such as synthetic aperture radar. It creates strong intensity fluctuations and hampers the analysis of images and the estimation of local radiometric, polarimetric, or interferometric properties. Synthetic aperture radar (SAR) processing chains thus often include a multilooking (i.e., averaging) filter for speckle reduction, at the expense of a strong resolution loss. Preservation of point-like and fine structures and textures requires to adapt locally the estimation. Nonlocal (NL)-means successfully adapt smoothing by deriving data-driven weights from the similarity between small image patches. The generalization of nonlocal approaches offers a flexible framework for resolution-preserving speckle reduction. We describe a general method, i.e., NL-SAR, that builds extended nonlocal neighborhoods for denoising amplitude, polarimetric, and/or interferometric SAR images. These neighborhoods are defined on the basis of pixel similarity as evaluated by multichannel comparison of patches. Several nonlocal estimations are performed, and the best one is locally selected to form a single restored image with good preservation of radar structures and discontinuities. The proposed method is fully automatic and handles single and multilook images, with or without interferometric or polarimetric channels. Efficient speckle reduction with very good resolution preservation is demonstrated both on numerical experiments using simulated data, airborne, and spaceborne radar images. The source code of a parallel implementation of NL-SAR is released with this paper.

Power Spectra of Very High Resolution SAR Amplitude Images of Urban Areas

With the launch of COSMO/SkyMed and TerraSARX missions, very high resolution (VHR) Synthetic Aperture Radar (SAR) images of urban areas have become routinely available. In particular, in the spotlight acquisition mode, COSMO/SkyMed SARs are able to obtain a resolution even better than one meter. Accordingly, in principle a huge amount of information on objects present in the urban scenario can be extracted from such images; however, due to the involved interaction between incident electromagnetic wave and imaged scene, direct interpretation of VHR SAR images is not straightforward, and different methods to analyze them are being developed by the scientific community. In this paper, we explore the potentiality of using the power spectral density (PSD) of VHR SAR amplitude images of urban areas for information extraction. First, we propose a theoretical model of the PSD of such images, and then we test the validity of the developed model on COSMO/SkyMed spotlight SAR images of urban areas. Finally, some suggestions are provided on how the parameters of the obtained PSD can be exploited to extract useful information from VHR SAR amplitude images of urban areas.

SAR Image Compression Using Multiscale Dictionary Learning and Sparse Representation

In this letter, we focus on a new compression scheme for synthetic aperture radar (SAR) amplitude images. The last decade has seen a growing interest in the study of dictionary learning and sparse representation, which have been proved to perform well on natural image compression. Because of the special techniques of radar imaging, SAR images have some distinct properties when compared with natural images that can affect the design of a compression method. First, we introduce SAR properties, sparse representation, and dictionary learning theories. Second, we propose a novel SAR image compression scheme by using multiscale dictionaries. The experimental results carried out on amplitude SAR images reveal that, when compared with JPEG, JPEG2000, and a single-scale dictionary-based compression scheme, the proposed method is better for preserving the important features of SAR images with a competitive compression performance.