Three-Order Tucker Decomposition and Reconstruction Detector for Unsupervised Hyperspectral Change Detection

Abstract

Change detection from multitemporal hyperspectral images has attracted great attention. Most traditional methods using spectral information for change detection treat a hyperspectral image as a two-dimensional matrix and do not take into account inherently structure information of spectrum, which leads to limited detection accuracy. To better approximate both spectral and spatial information, a novel three-order Tucker decomposition and reconstruction detector is proposed for hyperspectral change detection. Initially, Tucker decomposition and reconstruction strategies are used to eliminate the influence of various factors in a multitemporal dataset. Specifically, a singular value accumulation strategy is used to determine principal components in factor matrices. Meanwhile, a spectral angle is used to analyze spectral change after tensor processing in different domains. Finally, a new detector is designed to further improve the detection accuracy. Experiments conducted on five real hyperspectral datasets demonstrate that the proposed detector achieves a better detection performance.