A NOVEL FUSION-BASED UNSUPERVISED APPROACH FOR MULTISPECTRAL IMAGE CHANGE DETECTION WITH SALIENCY MAPS

Abstract. To fully utilize the spectral information and remove noise in multispectral image change detection, A fusion-based unsupervised approach, which exploits NSCT (Nonsubsampled Contourlet Transform) and multi-scale saliency maps for detecting changed areas by using multispectral images is presented in this paper. Firstly, aiming at make full use of multispectral information, each band of the multitemporal images is applied to get an initial difference image set (IDIS), which is then decomposed into several low-pass approximation and high-pass directional sub bands by NSCT; In order to remove most of the noise, saliency maps of each sub bands and each scales are obtained by processing only the low-frequency sub-band coefficients of the decomposed image; Finally the binary change map is extracted by using a novel inter-scale and inter-band fusion method. Experimental results validate the superior performance of the proposed approach with respect to several state-of-the-art change detection techniques.