A Patch Tensor-Based Change Detection Method for Hyperspectral Images
Zengfu Hou, Wei Li, Qian Du
Abstract
With the increasing of hyperspectral datasets, multi-temporal hyperspectral change detection has gradually attracted re-searcher's attention. Most of traditional change detection methods only consider spectral information, but ignore importance of spatial structure information, which leads to low detection accuracy. In this work, a novel patch tensor-based change detection method (PTCD) is proposed for hyperspectral imagery to make full use of spatial structure information. Firstly, the tensor decomposition and reconstruction strategies are used to eliminate influence of various factors in multi-temporal dataset. Meanwhile, patch-based strategy is adopted to incorporate the non-overlapping local similar property into the proposed method to exploit spatial structural information. Finally, a specially designed detector is adopted to further improve the detection accuracy. Experiments conducted on two real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.