Informative Change Detection by Unmixing for Hyperspectral Images

Abstract

Applying spectral unmixing on a series of multitemporal hyperspectral images for change detection has the potential to reveal important subpixel-level information, such as the abundance variation of each underlying material in a given location or the change in the distribution of materials throughout the scene, with time or resulting from significant events such as a natural disaster. However, change detection by spectral unmixing for hyperspectral images has not been extensively studied up to now, and most studies have been limited to specific cases and data sets. This is caused by the scarcity of real multitemporal hyperspectral data and the inherent difficulties in applying unmixing to multitemporal hyperspectral data in a coherent way. In this letter, we investigate change detection for hyperspectral images by spectral unmixing and systematically present the advantages that can be gained by using such an approach, supported by experimental studies conducted on carefully prepared synthetic data sets and also with real data sets.