{'fraction_non_alphanumeric': 0.014084507042253521, 'fraction_numerical': 0.0, 'mean_word_length': 5.536, '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}}
Automated Building Change Detection using multi-level filter in High Resolution Images

The Knowledge of urban building change are helpful to urban planning, traffic management and environmental studies. Advances in remote sensing science and the diversity of high resolution data hold great promise for the improvement of more precise information extraction and change detection, making change detection of LUCC at different scales from global to local scales more difficult. However, conventional remote sensing change detection techniques are inefficient due to the high spatial heterogeneity of the spectrum from the surface of an object, and to greater texture and clearer edges in the image. The requirement for real-time and effective change detection methods calls for the development of more automated techniques of change detection. A method of change detection based on integrating change vector analysis with NDVI filter noise filter ,object-based segmentation, and similarity calculation is presented for high spatial resolution data. It can be used to detect changes in buildings and streets quickly and automatically. The method in extracting and verifying changes in objects is presented, which are illustrated with an airborne linear scanner sensor image over the suburbs of Tokyo, Japan. Using these methods, we detect complex and fuzzy changes in high spatial resolution imagery at different times.The results suggest that change detection based on mulit-leveel filter and object similarity calibration is more reliable and efficient than post-classification change detection using high spatial resolution imagery.