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Remote Sensing
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Artificial Intelligence in Hyperspectral Remote Sensing Data Analysis
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Artificial Intelligence in Hyperspectral Remote Sensing Data Analysis

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1749

Special Issue Editors

Dr. Xue Wang

E-Mail Website
Guest Editor
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Interests: hyperspectral remote sensing; intelligent remote sensing interpretation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kun Tan

E-Mail Website
Guest Editor
The School of Geospatial Artificial Intelligence, East China Normal University, Shanghai 200241, China
Interests: hyperspectral remote sensing; environmental remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Haoyang Yu

E-Mail Website
Guest Editor
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: hyperspectral remote sensing; artificial intelligence; aerospace
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • spectral-spatial representation learning
  • multimodal fusion & explainable AI
  • hyperspectral unmixing & mixed pixel analysis
  • intelligent remote sensing applications

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Published Papers (3 papers)

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Research

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20 pages, 3459 KB  
Article
Diagnosis of Potassium Content in Rubber Leaves Based on Spatial–Spectral Feature Fusion at the Leaf Scale
by Xiaochuan Luo, Rongnian Tang, Chuang Li and Cheng Qian
Remote Sens. 2025, 17(17), 2977; https://doi.org/10.3390/rs17172977 - 27 Aug 2025
Viewed by 283
Abstract
Hyperspectral imaging (HSI) technology has attracted extensive attention in the field of nutrient diagnosis for rubber leaves. However, the mainstream method of extracting leaf average spectra ignores the leaf spatial information in hyperspectral imaging and dilutes the response characteristics exhibited by nutrient-sensitive local [...] Read more.
Hyperspectral imaging (HSI) technology has attracted extensive attention in the field of nutrient diagnosis for rubber leaves. However, the mainstream method of extracting leaf average spectra ignores the leaf spatial information in hyperspectral imaging and dilutes the response characteristics exhibited by nutrient-sensitive local areas of leaves, thereby limiting the accuracy of modeling. This study proposes a spatial–spectral feature fusion method based on leaf-scale sub-region segmentation. It introduces a clustering algorithm to divide leaf pixel spectra into several subclasses, and segments sub-regions on the leaf surface based on clustering results. By optimizing the modeling contribution weights of leaf sub-regions, it improves the modeling and generalization accuracy of potassium diagnosis for rubber leaves. Experiments have been carried out to verify the proposed method, which is based on spatial–spectral feature fusion to outperform those of average spectral modeling. Specifically, after pixel-level MSC preprocessing, when the spectra of rubber leaf pixel regions were clustered into nine subsets, the diagnostic accuracy of potassium content in rubber leaves reached 0.97, which is better than the 0.87 achieved by average spectral modeling. Additionally, precision, macro-F1, and macro-recall all reached 0.97, which is superior to the results of average spectral modeling. Moreover, the proposed method is also superior to the spatial–spectral feature fusion method that integrates texture features. The visualization results of leaf sub-region weights showed that strengthening the modeling contribution of leaf edge regions is conducive to improving the diagnostic accuracy of potassium in rubber leaves, which is consistent with the response pattern of leaves to potassium. Full article
(This article belongs to the Special Issue Artificial Intelligence in Hyperspectral Remote Sensing Data Analysis)
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35 pages, 58241 KB  
Article
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 363
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 [...] Read more.
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 l1/2 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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Review

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44 pages, 3439 KB  
Review
Conventional to Deep Learning Methods for Hyperspectral Unmixing: A Review
by Jinlin Zou, Hongwei Qu and Peng Zhang
Remote Sens. 2025, 17(17), 2968; https://doi.org/10.3390/rs17172968 - 27 Aug 2025
Viewed by 425
Abstract
Hyperspectral images often contain many mixed pixels, primarily resulting from their inherent complexity and low spatial resolution. To enhance surface classification and improve sub-pixel target detection accuracy, hyperspectral unmixing technology has consistently become a topical issue. This review provides a comprehensive overview of [...] Read more.
Hyperspectral images often contain many mixed pixels, primarily resulting from their inherent complexity and low spatial resolution. To enhance surface classification and improve sub-pixel target detection accuracy, hyperspectral unmixing technology has consistently become a topical issue. This review provides a comprehensive overview of methodologies for hyperspectral unmixing, from traditional to advanced deep learning approaches. A systematic analysis of various challenges is presented, clarifying underlying principles and evaluating the strengths and limitations of prevalent algorithms. Hyperspectral unmixing is critical for interpreting spectral imagery but faces significant challenges: limited ground-truth data, spectral variability, nonlinear mixing effects, computational demands, and barriers to practical commercialization. Future progress requires bridging the gap to applications through user-centric solutions and integrating multi-modal and multi-temporal data. Research priorities include uncertainty quantification, transfer learning for generalization, neuromorphic edge computing, and developing tuning-free foundation models for cross-scenario robustness. This paper is designed to foster the commercial application of hyperspectral unmixing algorithms and to offer robust support for engineering applications within the hyperspectral remote sensing domain. Full article
(This article belongs to the Special Issue Artificial Intelligence in Hyperspectral Remote Sensing Data Analysis)
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