Abstract
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.
Highlights
Remote sensing image change detection is the technique of qualitatively or quantitatively analyzing and determining the characteristics of surface change in multi-temporal remote sensing images of the same geographical area but from different times(Singh, 1989)
DeNSCT employs both the high-pass and low-pass sub band for change detection, it can fully expresses the details information of the changed object, but it’s hard to decide the portion of the high-pass sub band
An unsupervised approach with Nonsubsampled Contourlet Transform and multi-scale saliency maps change detection method is proposed for multispectral images
Summary
Remote sensing image change detection is the technique of qualitatively or quantitatively analyzing and determining the characteristics of surface change in multi-temporal remote sensing images of the same geographical area but from different times(Singh, 1989). Since the quality of remote sensing images is susceptible to seasonal change, atmospheric radiation and other factors, how to accurately determine the changed and unchanged areas from the image pairs still remains a difficult problem. To address the problems discussed above, in this paper, we proposed a fusion-based unsupervised change detection method with saliency maps for multispectral images. Firstly each band of the multitemporal images is applied to get an IDIS, which is decomposed into several lowpass approximation and high-pass directional sub bands by NSCT; saliency maps of each sub bands and each scales are obtained by processing only the low-frequency sub-band coefficients of the decomposed image; The final change map is extracted by a novel inter-scale and inter-band fusion method. Experimental results validate the effectiveness and feasibility of the proposed method
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