Next Article in Journal
Mapping Coral Reef Habitats with ICESat-2 and Satellite Imagery: A Novel Spectral Unmixing Approach Compared to Machine Learning
Previous Article in Journal
Shallow Sea Bathymetric Inversion of Active–Passive Satellite Remote Sensing Data Based on Virtual Control Point Inverse Distance Weighting
Previous Article in Special Issue
HyperTransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Hyperspectral Image Classification
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 17
Issue 21
10.3390/rs17213622
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing

by
Chong Zhao
1,2,
Jinlin Wang
1,3,*,
Qingqing Qiao
1,
Kefa Zhou
3,
Jiantao Bi
3,
Qing Zhang
3,
Wei Wang
3,
Dong Li
4,
Tao Liao
1,2,
Chao Li
5,
Heshun Qiu
2,3 and
Guangjun Qu
2,3
1
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China
4
School of Future Technology, China University of Geosciences, Wuhan 430074, China
5
Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(21), 3622; https://doi.org/10.3390/rs17213622 (registering DOI)
Submission received: 30 September 2025 / Revised: 24 October 2025 / Accepted: 30 October 2025 / Published: 31 October 2025
(This article belongs to the Special Issue AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land)
Versions Notes

Abstract

Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition. However, in geological mineral exploration, existing unmixing methods often fail to explicitly identify informative spectral bands, lack inter-layer information transfer mechanisms, and overlook the physical constraints intrinsic to the unmixing process. These issues result in limited directionality, sparsity, and interpretability. To address these limitations, this paper proposes a novel model, CResDAE, based on a deep autoencoder architecture. The encoder integrates a channel attention mechanism and deep residual modules to enhance its ability to assign adaptive weights to spectral bands in geological hyperspectral unmixing tasks. The model is evaluated by comparing its performance with traditional and deep learning-based unmixing methods on synthetic datasets, and through a comparative analysis with a nonlinear autoencoder on the Urban hyperspectral scene. Experimental results show that CResDAE consistently outperforms both conventional and deep learning counterparts. Finally, CResDAE is applied to GF-5 hyperspectral imagery from Yunnan Province, China, where it effectively distinguishes surface materials such as Forest, Grassland, Silicate, Carbonate, and Sulfate, offering reliable data support for geological surveys and mineral exploration in covered regions.
Keywords: deep autoencoder; attention mechanism; hyperspectral unmixing; abundance estimation deep autoencoder; attention mechanism; hyperspectral unmixing; abundance estimation

Share and Cite

MDPI and ACS Style

Zhao, C.; Wang, J.; Qiao, Q.; Zhou, K.; Bi, J.; Zhang, Q.; Wang, W.; Li, D.; Liao, T.; Li, C.; et al. CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing. Remote Sens. 2025, 17, 3622. https://doi.org/10.3390/rs17213622

AMA Style

Zhao C, Wang J, Qiao Q, Zhou K, Bi J, Zhang Q, Wang W, Li D, Liao T, Li C, et al. CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing. Remote Sensing. 2025; 17(21):3622. https://doi.org/10.3390/rs17213622

Chicago/Turabian Style

Zhao, Chong, Jinlin Wang, Qingqing Qiao, Kefa Zhou, Jiantao Bi, Qing Zhang, Wei Wang, Dong Li, Tao Liao, Chao Li, and et al. 2025. "CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing" Remote Sensing 17, no. 21: 3622. https://doi.org/10.3390/rs17213622

APA Style

Zhao, C., Wang, J., Qiao, Q., Zhou, K., Bi, J., Zhang, Q., Wang, W., Li, D., Liao, T., Li, C., Qiu, H., & Qu, G. (2025). CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing. Remote Sensing, 17(21), 3622. https://doi.org/10.3390/rs17213622

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop