{'fraction_non_alphanumeric': 0.01717171717171717, 'fraction_numerical': 0.0, 'mean_word_length': 4.728323699421965, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Registration-based change detection for SAR images

ABSTRACT This paper presents an efficient change detection approach for Synthetic Aperture Radar (SAR) images. The basic idea of this approach is to use the log ratio of the two images for change detection after being registered with Scale-Invariant Feature Transform (SIFT). These two images are a reference image and another image for the same area acquired at a different time. The log ratio variations include changes in certain areas corresponding to the natural changes in the test image. Usually, SAR images contain some sort of noise. So, there is a need for a denoising process prior to estimating the log ratio to enhance the change detection results. A segmentation process is performed on the test image based on the log ratio values. Large values in the log ratio image correspond to detected changes in the test image. Simulation results on SAR images for a region of Jeddah demonstrate the success of the proposed approach.