A spectral-spatial method based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection-Reference-Cited by-同舟云学术

A spectral-spatial method based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection

Published:2017-04-09 Issue:14 Volume:38 Page:4047-4068
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Zhang Lili¹²,Zhao Chunhui¹

Affiliation:

1. Department of Signal and Information Processing, Harbin Engineering University, College of Information and Communication Engineering, Harbin, China

2. Department of Electronic Information Engineering, Daqing Normal University, College of Mechanical and Electrical Engineering, Daqing, China

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Program Guiding Science and Technology of Daqing

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2017.1312620

Reference37 articles.

1. A support vector method for anomaly detection in hyperspectral imagery

2. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

3. Comparative evaluation of hyperspectral anomaly detectors in different types of background

4. Kernel-based methods for hyperspectral image classification

5. Hyperspectral Imaging

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