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

Zengfu Hou, Wei Li, Ran Tao, Qian Du

科研成果: 期刊稿件文章同行评审

52 引用 (Scopus)

摘要

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.

源语言英语
文章编号9451632
页(从-至)6194-6205
页数12
期刊IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
14
DOI
出版状态已出版 - 2021

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Hou, Z., Li, W., Tao, R., & Du, Q. (2021). Three-Order Tucker Decomposition and Reconstruction Detector for Unsupervised Hyperspectral Change Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 6194-6205. 文章 9451632. https://doi.org/10.1109/JSTARS.2021.3088438