{'fraction_non_alphanumeric': 0.023378582202111614, 'fraction_numerical': 0.0022624434389140274, 'mean_word_length': 5.702020202020202, '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}}
An MFF-SLIC Hybrid Superpixel Segmentation Method with Multi-Source RS Data for Rock Surface Extraction

Multi-Source RS data integration is a crucial technology for rock surface extraction in geology. Both Terrestrial laser scanning (TLS) and Photogrammetry are primary non-contact active measurement techniques. In order to extract comprehensive and accurate rock surface information by the integration of TLS point cloud and digital images, the segmentation based on the integrated results generated by registration is the crux. This paper presents a Multi-Features Fusion for Simple Linear Iterative Clustering (MFF-SLIC) hybrid superpixel segmentation algorithm to extract the rock surface accurately. The MFF-SLIC algorithm mainly includes three contents: (1) Mapping relationship construction for TLS point cloud and digital images; (2) Distance measure model establishment with multi-features for initial superpixel segmentation; (3) Hierarchical and optimized clustering for superpixels. The proposed method was verified with the columnar basalt data, which is acquired in Guabushan Geopark in China. The results demonstrate that the segmentation method could be used for rock surface extraction with high precision and efficiency, the result of which would be prepared for further geological statistics and analysis.