A split-based approach to unsupervised change detection in large-size SAR images

This paper presents a novel split-based approach to automatic and unsupervised detection of changes caused by tsunamis in large-size multitemporal SAR images. Unlike standard methods, the proposed approach can detect in a consistent and reliable way changes in images of large size also when the prior probability of the class of changed pixels is very small (and therefore the extension of the changed area is small). The method is based on: i) pre-processing of images and comparison; ii) sea identification and masking; iii) split-based analysis. The proposed system has been developed for properly identifying damages induced by tsunamis along coastal areas. Nevertheless presented approach is general and can be used (with small modifications) for damage assessment in different kinds of problems with different types of multitemporal remote sensing images. Experimental results obtained on multitemporal RADARSAT-1 SAR images of the Sumatra Island (Indonesia) confirm the effectiveness of the proposed split-based approach.