Processing of Sliding Spotlight and TOPS SAR Data Using Baseband Azimuth Scaling

This paper presents an efficient phase preserving processor for the focusing of data acquired in sliding spotlight and TOPS (Terrain Observation by Progressive Scans) imaging modes. They share in common a linear variation of the Doppler centroid along the azimuth dimension, which is due to a steering of the antenna (either mechanically or electronically) throughout the data take. Existing approaches for the azimuth processing can become inefficient due to the additional processing to overcome the folding in the focused domain. In this paper a new azimuth scaling approach is presented to perform the azimuth processing, whose kernel is exactly the same for sliding spotlight and TOPS modes. The possibility to use the proposed approach to process ScanSAR data, as well as a discussion concerning staring spotlight, are also included. Simulations with point-targets and real data acquired by TerraSAR-X in sliding spotlight and TOPS modes are used to validate the developed algorithm

Monitoring Land Surface Deformation with Satellite ScanSAR Images: Case Studies on Large Earthquakes in China

This chapter presents a new application of scanning interferometric synthetic aperture radar (ScanSAR) interferometry in monitoring land surface deformation caused by large earthquakes. To make better use of the ScanSAR data and obtain a wider deformation observation, this research studied and analyzed certain key elements of ScanSAR interferometry, including coherence, co-registering, methods of removing orbit errors, correction of atmosphere effects, and geoid undulation. The wide swath mode (WSM) is also known as the ScanSAR mode by which synthetic aperture time is shared by adjacent sub-swaths and azimuth resolution that is traded off for a wider coverage. So, it is possible to monitor a larger area of earthquake deformation. In this study, we obtained ScanSAR and Image Mode (IM) data and analyzed coherence, co-registering, methods of removing orbit errors, correction of atmosphere effects, and geoid undulation to monitor land surface deformation caused by large earthquakes in the 405 × 405 km field of the Wenchuan earthquake and Yutian earthquake, respectively, in China. The results obtained agree well with that of the investigations of the crustal motion in the study areas

A Comparison of Fixed Threshold CFAR and CNN Ship Detection Methods for S-band NovaSAR Images

NovaSAR is a commercial S-band Synthetic Aperture Radar (SAR) small satellite, built and operated by SSTL in the UK. One of its primary mission objectives is to carry out maritime surveillance and monitoring for security and defence applications. An investigation was carried out into comparing and contrasting conventional and new methods to perform automated ship detection in NovaSAR images. The outcome of this investigation could show the potential effectiveness of ship detection using spaceborne S-band SAR for Maritime Domain Awareness (MDA). The conventional approach is to apply a suitable distribution model to characterise sea surface clutter, followed by the implementation of a fixed threshold, Constant False Alarm Rate (CFAR) detection algorithm. In comparison, a RetinaNet-based convolutional neural network (CNN)solution was developed and trained on an open-source C-band dataset in order to determine the validity of applying non-native training data to S-band imagery. The detection performance was then compared with the CFAR technique, finding that for two selected test acquisitions a CNN-based ship detection algorithm was able to outperform a fixed threshold, CFAR-based method in the absence of native training data. CNN ship detection performance was further improved by applying transfer learning to a native S-band NovaSAR image dataset

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

Kinematic fault slip evolution source models of the 2008 M7.9 Wenchuan earthquake in China from SAR interferometry, GPS and teleseismic analysis and implications for Longmen Shan tectonics

The M_w 7.9 2008 Wenchuan earthquake ruptured about 280 km of faults in the Longmen Shan of Sichuan province, China, at the eastern edge of the Tibetan Plateau. We use teleseismic waveforms with geodetic data from Global Positioning System, synthetic aperture radar interferometry and image amplitude correlation to produce a source model of this earthquake. The model describes evolution of fault slip during the earthquake. The geodetic data constrains the spatial distribution of fault slip and the seismic waveforms constrain mostly the time evolution of slip. We find that the earthquake started with largely thrust motion on an imbricate system of faults beneath the central Longmen Shan, including the Beichuan Fault and Pengguan Fault, with fault slip at depth extending up to 50 km northwest of the mountain front. The fault ruptures continued northeast along the Beichuan Fault with more oblique slip (right-lateral and thrust) and the proportion of lateral motion increasing in the northern Longmen Shan. The northernmost fault segment has a much steeper dip, consistent with nearly pure strike-slip motion. The kinematic source model shows that the rupture propagated to the northeast at about 2.5–3.0 km s^(−1), producing a cascade of subevents with a total duration of about 110 s. The complex fault ruptures caused shortening and uplift of the extremely steep central Longmen Shan, which supports models where the steep edge of the plateau is formed by thrusting over the strong crust of the Sichuan Basin