Novel deep learning based SAR image processing - Wu, Xiguang (Nanyang Technological University, 2024)
In recent years, deep learning techniques have been widely used. However, in the study of synthetic aperture radar (SAR) terrestrial water detection, it is still a difficult task to support the training of deep network models due to the challenge of data acquisition and small sample size. This dissertation constructs and tests a SAR terrestrial water detection dataset Lake-SAR, which contains 30 scenes of Sentinel-1 SAR images covering 15 lakes such as Qinghai Lake and Poyang Lake, involving 9 provinces in China, with types including wind free area water and low wind area water. Meanwhile, this dissertation conducted experiments using classical deep learning image segmentation algorithms, among which the U-Net network has the best performance with an overall accuracy of 90.3%. The experimental comparative analysis forms the index benchmark, which can facilitate other scholars to further develop SAR land water detection related research on the basis of this dataset.