{'fraction_non_alphanumeric': 0.020903573836817263, 'fraction_numerical': 0.0, 'mean_word_length': 5.057142857142857, '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}}
Contour detection in high-resolution polarimetric SAR images

Automatic contour detection in SAR images is a difficult problem due to the presence of speckle. Several detectors exploiting the statistics of speckle in uniform regions have been already presented in literature. However, these were mainly applied to multi-look low-resolution imagery. This paper describes two new CFAR contour detectors for high-resolution single-look polarimetric SAR images. They are based on multi-variate statistical hypothesis tests. Failing of the test indicates the presence of an edge. A test for difference in means on log-intensity images and difference in variance on complex (SLC) images are used. Both tests take into account the interchannel covariance matrix which makes them a powerful tool for contour detection in multi-channel SAR images. Spatial correlation jeopardizes the CFAR character of the detectors. This problem is often neglected. In this paper its influence on the detectors is studied and eliminated. The localisation of detected edges is improved using a directional morphological filter. Different methods to fuse the results of the two detectors are explored and compared. Results obtained on a polarimetric L-band E-SAR image are presented. Most contours are well detected. Narrow lines on a uniform background remain undetected. Although the detector was developed to detect edges only between uniform areas, it also detects edges between textured and uniform areas.