Integrating spatial & spectral information for change detection in hyperspectral imagery

Abstract

Change detection (CD) is an important topic in the remote sensing community. Although many CD works exist using spatial information or spectral information only, few works have incorporated both in the CD process. We propose a fused spatial-spectral feature vector for use in a maximum likelihood correlation coefficient (MLCC)-based change detector where the resulting test statistic provides the ability to label changes as departures or arrivals relative to the reference image. Results show that incorporating both spatial and spectral information has an advantage over either one independently. Additionally, incorporating spatial and spectral information in the CD process adds some robustness in the presence of misregistration errors.