{'fraction_non_alphanumeric': 0.023514851485148515, 'fraction_numerical': 0.0037128712871287127, 'mean_word_length': 5.271317829457364, '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}}
Decision tree approach to classify the fully polarimetric RADARSAT-2 data

Fully polarimetric Synthetic Aperture Radar (SAR) data provides the huge amount of information which is useful for classifying earth's surface information. Many techniques are available in the literature for land cover classification. Still, it is a very challenging task to identify various land cover classes. Therefore, in the paper, based on the polarimetric indexes, fully polarimetric SAR data has been used for the identification of various land cover types. Thus, a decision-tree-based approach has been proposed for land cover classification in this paper. The proposed algorithm of decision tree classification scheme is successfully implemented and tested on RADASAT-2 data and validated on another set of RADARSAT-2 data.