Improving Building Change Detection in VHR Remote Sensing Imagery by Combining Coarse Location and Co-Segmentation

Building change detection based on remote sensing imagery is a significant task for urban construction, management, and planning. Feature differences caused by changes are fundamental in building change detection, but the spectral and spatial disturbances of adjacent geo-objects that can extensively affect the results are not considered. Moreover, the diversity of building features often renders change detection difficult to implement accurately. In this study, an effective approach is proposed for the detection of individual changed buildings. The detection process mainly consists of two phases: (1) locating the local changed area with the differencing method and (2) detecting changed buildings by using a fuzzy clustering-guided co-segmentation algorithm. This framework is broadly applicable for detecting changed buildings with accurate edges even if their colors and shapes differ to some extent. The results of the comparative experiment show that the strategy proposed in this study can improve building change detection.

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