An approach based on discrete wavelet transform to unsupervised change detection in multispectral images

Change vector analysis (CVA) and Spectral Angle Mapper (SAM) are widely used for change detection in multitemporal multispectral images. CVA and SAM describe the difference from the perspective of vector magnitude and spectral angle, respectively. It has been proved that three change categories may occur in a changed pixel; however, CVA or SAM alone can only detect two of the three change categories properly. Hence, we propose a novel approach integrating the advantages of them to acquire a better change map. This approach, based on discrete wavelet transform (ABDWT, i.e. approach based on discrete wavelet transform), obtains two difference images by using CVA and SAM, and then yields a novel difference image by fusing them in the coefficients domains of discrete wavelet transform. Experimental results from a simulated and two real data sets validate the effectiveness of the proposed approach. In the first real data set, the proposed approach can identify 14,916 changed pixels while the best result of other methods is 14,806. In the second real data set, the proposed approach detects 3203 changed pixels, while the maximum of other methods is 3189.