MSIM: a change detection framework for damage assessment in natural disasters

This paper investigates how social images and change detection techniques can be applied to identify the damage caused by natural disasters for disaster assessment. We propose a multi-step image matching-based framework (MSIM), which takes advantages of fast clustering, near duplicate image detection and robust boundary matching for the change detection in disasters. We first model the social images by exploiting their tags and locations. After that, we propose a recursive 2-means algorithm over the new data model, and refine the near duplicate detection by local interest point-based matching over image pairs in neighboring clusters. Finally, we propose a novel boundary representation model called relative position annulus (RPA), which is robust to boundary rotation, location shift and editing operations. A new RPA matching method is proposed by extending dynamic time wrapping (DTW) from time to position annulus. We have done extensive experiments to evaluate the high effectiveness and efficiency of our approach.