Simultaneous change region and pattern identification for very high-resolution images

By taking advantages of fine details obtained by the improved spatial resolution, very high-resolution images are promising for detecting change regions and identifying change patterns. However, high overlaps between different change patterns and the complexities of multiclass classification make it difficult to reliably separate change features. A framework named simultaneous change region and pattern identification (SCRAPI) is proposed for simultaneously detecting change regions and identifying change patterns, whose components are aimed at capturing overlaps between change patterns and reducing overlaps driven by user-specific interests. To validate the effectiveness of SCRAPI, a supervised approach is illustrated within this framework, which starts with modeling the relationship between change features by interclass couples and intraclass couples, followed by metric learning where structural sparsity is captured by the mixed norm. Experiments demonstrate the effectiveness of the proposed approach. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)