{'fraction_non_alphanumeric': 0.023578363384188627, 'fraction_numerical': 0.0013869625520110957, 'mean_word_length': 4.869918699186992, '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}}
Detecting Cars in VHR SAR Images via Semantic CFAR Algorithm

In this letter, a novel semantic constant-false-alarm-rate (CFAR) method for the detection of cars from a very high resolution synthetic-aperture-radar (SAR) image is presented. The method not only employs the strong scattering features of the target which is used in CFAR but also employs the shadow features of the target. Furthermore, the semantic relationship between the strong scattering features and the shadow features is established to partly reduce the false alarm targets. By the experiments on the MiniSAR image of 4-in resolution, it is shown that the proposed semantic CFAR method outperforms the CFAR algorithm by a much lower false alarm rate.