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Artificial Intelligence in Hyperspectral Remote Sensing Data Analysis

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Dear Colleagues,

Hyperspectral remote sensing, which captures hundreds of contiguous narrow spectral bands across the electromagnetic spectrum, has emerged as a cornerstone technology for analyzing the surface with unparalleled spectral fidelity. By integrating artificial intelligence (AI), this field has undergone a paradigm shift, enabling the extraction of actionable insights from high-dimensional datasets that were previously intractable using conventional methods. AI techniques, particularly deep learning and machine learning, address the intrinsic challenges of hyperspectral data, such as the curse of dimensionality, spectral mixing, and noise, while unlocking novel capabilities for feature extraction, classification, and predictive modeling. AI-driven frameworks, including convolutional neural networks, transformers, and generative models, have revolutionized hyperspectral applications across diverse domains, including resource exploration, environmental monitor, urban application, precision agriculture, etc.

As hyperspectral sensors proliferate across satellites, drones, and ground platforms, AI bridges the gap between data complexity and actionable insights, driving innovation in urban, energy, ecology, and beyond, while democratizing access to advanced spectral analysis tools. This Special Issue will include studies covering artificial intelligence in hyperspectral remote sensing data analysis, including both the optimization and enhancement of hyperspectral interpretation algorithms and application case studies based on deep learning for hyperspectral data analysis. Articles may address, but are not limited, to the following topics:

Hyperspectral Pre-trained Models and Foundation Models;
Multimodal Fusion and Spectral–Spatial Feature Learning;
Explainable AI for Hyperspectral Remote Sensing;
Self-Supervised Spectral–Spatial Representation Learning;
Hyperspectral Classification and Change Detection;
Hyperspectral Unmixing and Mixed Pixel Analysis;
Hyperspectral Precision Agriculture;
Intelligent Hyperspectral Environmental Monitoring.

Dr. Xue Wang
Prof. Dr. Kun Tan
Dr. Haoyang Yu
Guest Editors

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35 pages, 58241 KiB

Open AccessArticle

DGMNet: Hyperspectral Unmixing Dual-Branch Network Integrating Adaptive Hop-Aware GCN and Neighborhood Offset Mamba

by Kewen Qu, Huiyang Wang, Mingming Ding, Xiaojuan Luo and Wenxing Bao

Remote Sens. 2025, 17(14), 2517; https://doi.org/10.3390/rs17142517 - 19 Jul 2025

Viewed by 300

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

l_{1 / 2}

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. Full article

(This article belongs to the Special Issue Artificial Intelligence in Hyperspectral Remote Sensing Data Analysis)

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