3D-CNN and semi-supervised based network for hyperspectral unmixing-Reference-Cited by-同舟云学术

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3D-CNN and semi-supervised based network for hyperspectral unmixing

Published:2023-12-28 Issue:1 Volume:45 Page:168-191
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Zhang Jinhua¹,Zhang Xiaohua¹,Fan Jiaxin²,Meng Hongyun³

Affiliation:

1. School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xian, Shaanxi, China

2. Business School, University of New South Wales, Sydney, Australia

3. School of Mathematics and Statistics, Xidian University, Xian, Shaanxi, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2290998

Reference34 articles.

1. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

2. Arjovsky M. S. Chintala and L. Bottou. 2017. “Wasserstein Generative Adversarial Networks.” proceedings.mlr.press. July. https://proceedings.mlr.press/v70/arjovsky17a.html.

3. Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing

4. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

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