hyperspectral-change-detection-ptcd's Introduction

Hyperspectral-Change-Detection-PTCD

Paper: A Patch Tensor-based Change Detection Method for Hyperspectral Images

Fig.1. Framework of proposed PTCD Method.

Prerequisites

MATLAB R2018a

Tip: There may be problems when using Matlab 2016 for tensor processing. Please use 2018 and above version.

Source

Paper Download:

IGARSS 2021 Paper

Hermiston dataset:

Citation

This IGARSS2021 conference paper, you can downlowad from IEEE explore.

If these codes and dataset are helpful for you, please cite this paper:

BibTex Format:

@ARTICLE{9451632,  author={Hou, Zengfu and Li, Wei and Tao, Ran and Du, Qian},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title={Three-Order Tucker Decomposition and Reconstruction Detector for Unsupervised Hyperspectral Change Detection},
year={2021},
volume={14},
number={},
pages={6194-6205}, 
doi={10.1109/JSTARS.2021.3088438}}

Plain Text Format:

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

Other Related Papers

[1] Zengfu Hou, Wei Li, Lu Li, Ran Tao, and Qian Du. Hyperspectral Change Detection Based on Multiple Morphological Profiles [J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, doi:10.1109/TGRS.2021.3090802

[2] Zengfu Hou, Wei Li, Ran Tao, and Qian Du. Three-Order Tucker Decomposition and Reconstruction Detector for Unsupervised Hyperspectral Change Detection [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, doi: 10.1109/JSTARS.2021.3088438]