Strategies Combining Spectral Angle Mapper and Change Vector Analysis to Unsupervised Change Detection in Multispectral Images

Abstract

Change vector analysis (CVA) has been widely used in change detection of multitemporal multispectral images, whereas the use of a spectral angle mapper (SAM) is always ignored. CVA and SAM describe the difference from the perspective of vector magnitude and direction, respectively. Combining CVA and SAM can fully utilize the spectral vector and obtain a better change map. In this context, two strategies have been proposed: 1) a novel hybrid feature vector is constructed using a spectral angle and a change vector, which is utilized in SAM and CVA, respectively, and 2) a novel difference image is generated by using the autoadapted fusion strategy, which fuses the difference images acquired by utilizing CVA and SAM. During the fusion process, the autoadapted weight is defined by employing the entropy of the difference image. Experimental results on both simulated images and multitemporal multispectral images validate the effectiveness of the proposed methods.