{'fraction_non_alphanumeric': 0.021164021164021163, 'fraction_numerical': 0.007558578987150416, 'mean_word_length': 5.3349282296650715, '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}}
Classification of Terrestrial Laser Scanning Data With Density-Adaptive Geometric Features

Point cloud classification is a crucial procedure in ground scene interpretation. In this letter, density-adaptive geometric features are proposed for the classification of terrestrial laser scanning data, with the problem brought by point density variation as one of the main concerns. For each point, point spacing is estimated, respectively, based upon the distance to scanner position and the angular resolution, and then used as neighborhood scale basis to generate the search range of optimal radius. In feature extraction, we modify some common geometric features to adapt to density variation, e.g., a polar projection grid is proposed to generate projection features instead of commonly used rectangular grid. The polar grid can make sure similar number of laser beams passing through each grid. An evaluation involving five classifiers is carried out in an outdoor scene captured by Reigl-VZ400 scanner and the results show density-adaptive features have better and more stable performances than features without considering density variation, with the highest overall accuracy of 95.95%. Moreover, the proposed features perform well on the recognition of buildings from a large distance (more than 300 m in this letter).