A segmentation based change detection method for high resolution remote sensing image

Abstract

This paper proposes a segmentation based change detection method for high resolution remote sensing images. Firstly, one of the multi-temporal images is segmented by a new image segmentation algorithm, in which, the particle swarm optimization algorithm (PSO) is adopted to obtain the optimal segmentation results. Secondly, the same segmentation mask is used to extract image regions from the other temporal image. Thirdly, the spectral, shape, texture and vegetation index features are extracted from image regions to identify the changed image regions. The performance of the proposed change detection method is assessed by comparing with 4 other widely used change detection methods on two data sets of multi-temporal ZiYuan-3 (ZY-3) high resolution remote sensing images. Experimental results show that accurate image regions and satisfied changed areas can be acquired by our proposed method.