A sparse and spectral smooth regularized low-rank tensor decomposition method for hyperspectral target detection-Reference-Cited by-同舟云学术

A sparse and spectral smooth regularized low-rank tensor decomposition method for hyperspectral target detection

Published:2022-06-18 Issue:12 Volume:43 Page:4608-4629
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Zhao Chunhui¹²,Wang Mingxing¹²,Feng Shou¹²

Affiliation:

1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang, China

2. Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, Heilongjiang, China

Funder

National Natural Science Foundation of China

the Heilongjiang Provincial Natural Science Foundation of China

Heilongjiang Postdoctoral Foundation

the Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2114110

Reference28 articles.

1. New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis

2. Bag-of-visual-words based feature extraction for SAR target classification

3. Deep feature extraction and combination for synthetic aperture radar target classification

4. SAR-Oriented Visual Saliency Model and Directed Acyclic Graph Support Vector Metric Based Target Classification

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