@inproceedings{b3d2961eb1084cee9fb83d454c0e5e52,
title = "A PATCH TENSOR-BASED CHANGE DETECTION METHOD FOR HYPERSPECTRAL IMAGES",
abstract = "With the increasing of hyperspectral datasets, multi-temporal hyperspectral change detection has gradually attracted researcher{\textquoteright}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.",
keywords = "change detection, Hyperspectral, patch strategy, tensor",
author = "Zengfu Hou and Wei Li and Qian Du",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
doi = "10.1109/IGARSS47720.2021.9554630",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4328--4331",
booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "United States",
}