Generative Adversarial Autoencoder Network for Anti-Shadow Hyperspectral Unmixing-Reference-Cited by-同舟云学术

Generative Adversarial Autoencoder Network for Anti-Shadow Hyperspectral Unmixing

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Sun Bin¹^ORCID,Su Yuanchao²^ORCID,Sun He³^ORCID,Bai Jinying⁴,Li Pengfei¹^ORCID,Liu Feng¹,Liu Dongsheng⁴^ORCID

Affiliation:

1. College of Geomatics, Xi’an University of Science and Technology, Xi’an, China

2. Department of Remote Sensing, the College of Geomatics, and the College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, China

3. Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

4. Piesat Information Technology Company Ltd., Beijing, China

Funder

National Natural Science Foundation of China

Macau Young Scholars Program

University-Industry Collaborative Education Program of the Ministry of Education of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10533262.pdf?arnumber=10533262

Reference15 articles.

1. Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution

2. NSCKL: Normalized Spectral Clustering With Kernel-Based Learning for Semisupervised Hyperspectral Image Classification

3. DAAN: A Deep Autoencoder-Based Augmented Network for Blind Multilinear Hyperspectral Unmixing

4. Deep Autoencoders With Multitask Learning for Bilinear Hyperspectral Unmixing

5. Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery