{'fraction_non_alphanumeric': 0.014418125643666324, 'fraction_numerical': 0.005149330587023687, 'mean_word_length': 5.191082802547771, '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}}
A Deep Learning Approach to Ship Detection and Characterization from Multiresolution Satellite SAR Images

Ship detection using synthetic aperture radar images is a key technology in maritime surveillance applications. In addition to the position of the vessel, the characterization of the target (length, width and orientation) is often a requirement. In this paper, we present a deep learning architecture for object detection we developed by modifying the popular YOLOv3 architecture to apply to vessel detection and parameter estimation from SAR images. The proposed architecture was trained and tested on a large dataset of SAR images defined in this work. It contains images covering a wide range of spatial resolutions (pixel spacing ranging from 1.5m to 50m) and labelled with oriented bounding boxes to associate to each vessel not only its position but also size and orientation. The obtained results are very promising and confirm the validity of the approach.