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Open AccessArticle
DGMNet: Hyperspectral Unmixing Dual-Branch Network Integrating Adaptive Hop-Aware GCN and Neighborhood Offset Mamba
by
Kewen Qu
Kewen Qu 1,2,*,†,
Huiyang Wang
Huiyang Wang 1,2,†
,
Mingming Ding
Mingming Ding 1,2,
Xiaojuan Luo
Xiaojuan Luo 1,2 and
Wenxing Bao
Wenxing Bao 1,2
1
The School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
2
The Image and Intelligence Information Processing Innovation Team of the National Ethnic Affairs Commission of China, Yinchuan 750021, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2025, 17(14), 2517; https://doi.org/10.3390/rs17142517 (registering DOI)
Submission received: 18 June 2025
/
Revised: 13 July 2025
/
Accepted: 17 July 2025
/
Published: 19 July 2025
Abstract
Hyperspectral sparse unmixing (SU) networks have recently received considerable attention due to their model hyperspectral images (HSIs) with a priori spectral libraries and to capture nonlinear features through deep networks. This method effectively avoids errors associated with endmember extraction, and enhances the unmixing performance via nonlinear modeling. However, two major challenges remain: the use of large spectral libraries with high coherence leads to computational redundancy and performance degradation; moreover, certain feature extraction models, such as Transformer, while exhibiting strong representational capabilities, suffer from high computational complexity. To address these limitations, this paper proposes a hyperspectral unmixing dual-branch network integrating an adaptive hop-aware GCN and neighborhood offset Mamba that is termed DGMNet. Specifically, DGMNet consists of two parallel branches. The first branch employs the adaptive hop-neighborhood-aware GCN (AHNAGC) module to model global spatial features. The second branch utilizes the neighborhood spatial offset Mamba (NSOM) module to capture fine-grained local spatial structures. Subsequently, the designed Mamba-enhanced dual-stream feature fusion (MEDFF) module fuses the global and local spatial features extracted from the two branches and performs spectral feature learning through a spectral attention mechanism. Moreover, DGMNet innovatively incorporates a spectral-library-pruning mechanism into the SU network and designs a new pruning strategy that accounts for the contribution of small-target endmembers, thereby enabling the dynamic selection of valid endmembers and reducing the computational redundancy. Finally, an improved ESS-Loss is proposed, which combines an enhanced total variation (ETV) with an sparsity constraint to effectively refine the model performance. The experimental results on two synthetic and five real datasets demonstrate the effectiveness and superiority of the proposed method compared with the state-of-the-art methods. Notably, experiments on the Shahu dataset from the Gaofen-5 satellite further demonstrated DGMNet’s robustness and generalization.
Share and Cite
MDPI and ACS Style
Qu, K.; Wang, H.; Ding, M.; Luo, X.; Bao, W.
DGMNet: Hyperspectral Unmixing Dual-Branch Network Integrating Adaptive Hop-Aware GCN and Neighborhood Offset Mamba. Remote Sens. 2025, 17, 2517.
https://doi.org/10.3390/rs17142517
AMA Style
Qu K, Wang H, Ding M, Luo X, Bao W.
DGMNet: Hyperspectral Unmixing Dual-Branch Network Integrating Adaptive Hop-Aware GCN and Neighborhood Offset Mamba. Remote Sensing. 2025; 17(14):2517.
https://doi.org/10.3390/rs17142517
Chicago/Turabian Style
Qu, Kewen, Huiyang Wang, Mingming Ding, Xiaojuan Luo, and Wenxing Bao.
2025. "DGMNet: Hyperspectral Unmixing Dual-Branch Network Integrating Adaptive Hop-Aware GCN and Neighborhood Offset Mamba" Remote Sensing 17, no. 14: 2517.
https://doi.org/10.3390/rs17142517
APA Style
Qu, K., Wang, H., Ding, M., Luo, X., & Bao, W.
(2025). DGMNet: Hyperspectral Unmixing Dual-Branch Network Integrating Adaptive Hop-Aware GCN and Neighborhood Offset Mamba. Remote Sensing, 17(14), 2517.
https://doi.org/10.3390/rs17142517
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