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Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 7955

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Special Issue Editors

Dr. Jiaxin Li

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Guest Editor

School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: data fusion; hyperspectral fusion; super resolution; change detection; registration; classification; denoising; unmixing; segmentation; anomaly detection; transformer; diffusion; self-supervised
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lianru Gao

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Guest Editor

Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: geophysical image processing; image classification; hyperspectral imaging; remote sensing; feature extraction; image resolution; learning (artificial intelligence); geophysical techniques; object detection; feedforward neural nets; optical radar; convolutional neural nets; image fusion; image reconstruction; image representation; remote sensing by laser beam; Bayes methods; Markov processes; aerosols; agriculture; air pollution; artificial satellites; atmospheric optics; convex programming; convolution
Special Issues, Collections and Topics in MDPI journals

Dr. Ke Zheng

E-Mail Website
Guest Editor Assistant

School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
Interests: image processing, machine learning, deep learning and their application in Earth Vision; remote sensing

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing enables the detection of targets with a high spectral resolution across narrow wavelength bands, combining spatial imagery with continuous spectral data. This technology allows for precise identification and offers a transformative perspective regarding the observation of Earth. Moreover, recent advances in artificial intelligence have further propelled progress in hyperspectral remote sensing, enhancing its theoretical foundations, technological capabilities, and practical applications across diverse fields.

This Special Issue seeks to showcase the cutting-edge developments in hyperspectral remote sensing, including theoretical innovations, technological breakthroughs, and novel applications. By compiling a collection of the latest studies, we aim to provide valuable insights for the remote sensing research community and foster future advancements in this dynamic field.

In this Special Issue, both original research articles and reviews are welcome. The research areas may include (but are not limited to) the following:

Hyperspectral low-level vision tasks (e.g., denoising, restoration, super-resolution, fusion);
Hyperspectral high-level tasks (e.g., classification, segmentation, anomaly detection);
The application of hyperspectral remote sensing in specific fields (e.g., precision agriculture, water resource management, mineral exploration).

Dr. Jiaxin Li
Prof. Dr. Lianru Gao
Guest Editors

Dr. Ke Zheng
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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

remote sensing
hyperspectral remote sensing
image processing
hyperspectral applications
artificial intelligence

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Published Papers (8 papers)

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13 pages, 5433 KB

Open AccessArticle

Applications of Airborne Hyperspectral Imagery in Rare Earth Element Exploration: A Case Study of the World-Class Bayan Obo Deposit, China

by Cai Liu, Junting Qiu, Junchuan Yu, Yanbo Zhao, Yuanquan Xu, Xin Zhang, Bin Chen, Rong Xu, Qianli Ma, Gang Liu and Jinzhong Yang

Remote Sens. 2026, 18(8), 1110; https://doi.org/10.3390/rs18081110 - 8 Apr 2026

Viewed by 261

Abstract

Rare earth elements (REEs) play an important role in emerging renewable energy technology, the production of advanced materials, energy conservation, and high-end manufacturing industries, making them an irreplaceable strategic resource. The diagnostic spectral absorption features of REEs in the visible and near-infrared spectrum can be effectively used for identifying the occurrences of REEs on the Earth’s surface. This study systematically compared three airborne hyperspectral sensors—HyMap, CASI-1500h, and AisaFENIX 1K—for detecting REEs in the Bayan Obo area of Inner Mongolia, China. The CASI-1500h imagery performed most effectively in identifying the locations of REEs among the three sensors evaluated here. Additionally, this study proposed a hyperspectral workflow for REE identification, which enabled the detection of REE-bearing minerals regardless of the host rock types—including carbonatites and associated dikes, fenite-syenites, and metamorphic feldspar-quartz sandstone. Laboratory-based spectroscopy and mineral chemistry analyses indicated that the absorption features of the REE-bearing mineral monazite within the 400–1000 nm range can be ascribed to Nd³⁺. This study demonstrates the potential of airborne hyperspectral technology for efficient and large-scale exploration of REE deposits. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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25 pages, 20821 KB

Open AccessArticle

Double-Attention Context Interactive Network for Hyperspectral Image Classification

by Nannan Hu, Zhongao Wang, Minghao Wang and Yuefeng Zhao

Remote Sens. 2026, 18(7), 1059; https://doi.org/10.3390/rs18071059 - 2 Apr 2026

Viewed by 335

Abstract

Convolution is still the main method for hyperspectral image classification, since it takes into account both spatial and spectral characteristics. However, the convolution relies on local perceptual computation, ignoring the effective discriminant of context association for classification. In this paper, we propose a Double-Attention Context Interactive Network (DACINet) for hyperspectral image classification. Specifically, a Context Interaction Fusion Module (CIFM) is designed to enhance long-range contextual dependencies. By stacking multiple 3D convolutional layers, the module progressively enlarges its receptive field, while cross-layer residual connections facilitate the integration of features from different contextual scales, thereby strengthening the model’s ability to capture complex relationships within the hyperspectral data. Then, a Channel–Spatial Double-Attention (CSDA) mechanism based on 3D is proposed for enhancing the two-dimensional spatial features and one-dimensional spectral features, respectively, and fusing the enhanced features. Furthermore, we also construct a hybrid convolutional layer, which combines 2D and 3D convolution to further enhance spectral bands on the basis of three-dimensional understanding. Extensive experiments on the widely used IP, UP, SA and HU datasets show that the proposed DACINet achieves superior classification accuracy, reaching Overall Accuracies of 96.78%, 97.77%, 99.53% and 86.67% respectively, outperforming other state-of-the-art models. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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28 pages, 5166 KB

Open AccessArticle

Hyperspectral Image Classification Using SIFANet: A Dual-Branch Structure Combining CNN and Transformer

by Yuannan Gui, Lu Xu, Dongping Ming, Yanfei Wei and Ming Huang

Remote Sens. 2026, 18(3), 398; https://doi.org/10.3390/rs18030398 - 24 Jan 2026

Viewed by 810

Abstract

The hyperspectral image (HSI) is rich in spectral information and has important applications in the field of ground objects classification. However, HSI data have high dimensions and variable spatial–spectral features, which make it difficult for some models to adequately extract the effective features. Recent studies have shown that fusing spatial and spectral features can significantly improve accuracy by exploiting multi-dimensional correlations. Based on this, this article proposes a spectral integration and focused attention network (SIFANet) with a two-branch structure. SIFANet captures the local spatial features and global spectral dependencies through the parallel-designed spatial feature extractor (SFE) and spectral sequence Transformer (SST), respectively. A cross-module attention fusion (CMAF) mechanism dynamically integrates features from both branches before final classification. Experiments on the Salinas dataset and Xiong’an hyperspectral dataset show that the overall accuracy on these two datasets is 99.89% and 99.79%, which is higher than the other models compared. The proposed method also had the lowest standard deviation of category accuracy and optimal computational efficiency metrics, demonstrating robust spatial–spectral feature integration for improved classification. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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29 pages, 73612 KB

Open AccessArticle

DNMF-AG: A Sparse Deep NMF Model with Adversarial Graph Regularization for Hyperspectral Unmixing

by Kewen Qu, Xiaojuan Luo and Wenxing Bao

Remote Sens. 2026, 18(1), 155; https://doi.org/10.3390/rs18010155 - 3 Jan 2026

Viewed by 650

Abstract

Hyperspectral unmixing (HU) aims to extract constituent information from mixed pixels and is a fundamental task in hyperspectral remote sensing. Deep non-negative matrix factorization (DNMF) has recently attracted attention for HU due to its hierarchical representation capability. However, existing DNMF-based methods are often sensitive to noise and outliers, and face limitations in incorporating prior knowledge, modeling feature structures, and enforcing sparsity constraints, which restrict their robustness, accuracy, and interpretability. To address these challenges, we propose a sparse deep NMF model with adversarial graph regularization for hyperspectral unmixing, termed DNMF-AG. Specifically, we design an adversarial graph regularizer that integrates local similarity and dissimilarity graphs to promote intraclass consistency and interclass separability in the spatial domain, thereby enhancing structural modeling and robustness. In addition, a Gram-based sparsity constraint is introduced to encourage sparse abundance representations by penalizing inner product correlations. To further improve robustness and computational efficiency, a truncated activation function is incorporated into the iterative update process, suppressing low-amplitude components and promoting zero entries in the abundance matrix. The overall model is optimized using the alternating direction method of multipliers (ADMM). Experimental results on multiple synthetic and real datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of estimation accuracy and robustness. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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25 pages, 8239 KB

Open AccessArticle

Weighted Total Variation for Hyperspectral Image Denoising Based on Hyper-Laplacian Scale Mixture Distribution

by Xiaoyu Yu, Jianli Zhao, Sheng Fang, Tianheng Zhang, Liang Li and Xinyue Huang

Remote Sens. 2026, 18(1), 135; https://doi.org/10.3390/rs18010135 - 31 Dec 2025

Viewed by 778

Abstract

Conventional total variation (TV) regularization methods based on Laplacian or fixed-scale Hyper-Laplacian priors impose uniform sparsity penalties on gradients. These uniform penalties fail to capture the heterogeneous sparsity characteristics across different regions and directions, often leading to the over-smoothing of edges and loss of fine details. To address this limitation, we propose a novel regularization Hyper-Laplacian Adaptive Weighted Total Variation (HLAWTV). The proposed regularization employs a proportional mixture of Hyper-Laplacian distributions to dynamically adapt the sparsity decay rate based on image structure. Simultaneously, the adaptive weights can be adjusted based on local gradient statistics and exhibit strong robustness in texture preservation when facing different datasets and noise. Then, we propose an hyperspectral image (HSI) denoising method based on the HLAWTV regularizer. Extensive experiments on both synthetic and real hyperspectral datasets demonstrate that our denoising method consistently outperforms state-of-the-art methods in terms of quantitative metrics and visual quality. Moreover, incorporating our adaptive weighting mechanism into existing TV-based models yields significant performance gains, confirming the generality and robustness of the proposed approach. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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32 pages, 8264 KB

Open AccessArticle

SATRNet: Self-Attention-Aided Deep Unfolding Tensor Representation Network for Robust Hyperspectral Anomaly Detection

by Jing Yang, Jianbin Zhao, Lu Chen, Haorui Ning and Ying Li

Remote Sens. 2025, 17(18), 3137; https://doi.org/10.3390/rs17183137 - 10 Sep 2025

Viewed by 1180

Abstract

Hyperspectral anomaly detection (HAD) aims to separate subtle anomalies of a given hyperspectral image (HSI) from its background, which is a hot topic as well as a challenging inverse problem. Despite the significant success of the deep learning-based HAD methods, they are hard to interpret due to their black-box nature. Meanwhile, deep learning methods suffer from the identity mapping (IM) problem, referring to the network excessively focusing on the precise reconstruction of the background while neglecting the appropriate representation of anomalies. To this end, this paper proposes a self-attention-aided deep unfolding tensor representation network (SATRNet) for interpretable HAD by solving the tensor representation (TR)-based optimization model within the framework of deep networks. In particular, a Self-Attention Learning Module (SALM) was first designed to extract discriminative features of the input HSI. The HAD problem was then formulated as a tensor representation problem by exploring both the low-rankness of the background and the sparsity of the anomaly. A Weight Learning Module (WLM) exploring local details was also generated for precise background reconstruction. Finally, a deep network was built to solve the TR-based problem through unfolding and parameterizing the iterative optimization algorithm. The proposed SATRNet prevents the network from learning meaningless mappings, making the network interpretable to some extent while essentially solving the IM problem. The effectiveness of the proposed SATRNet is validated on 11 benchmark HSI datasets. Notably, the performance of SATRNet against adversarial attacks is also investigated in the experimentation, which is the first work exploring adversarial robustness in HAD to the best of our knowledge. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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23 pages, 11704 KB

Open AccessArticle

Reliable Task-Constrained Band Selection Method for Hyperspectral Anomaly Detection

by Genrui Zhang, Wenzheng Wang, Yuqi Han, Chenwei Deng and Xingshi Luo

Remote Sens. 2025, 17(17), 3081; https://doi.org/10.3390/rs17173081 - 4 Sep 2025

Viewed by 1778

Abstract

Hyperspectral band selection utilizes a crucial band subset to represent original data. In hyperspectral anomaly detection tailored for specific tasks, detection performance can be enhanced by pre-selecting a subset of bands that are more representative. However, existing methods remain constrained in modeling spatial–spectral dependencies and simultaneously extracting distinct bands’ contribution from the established model, thus struggling to balance effectiveness and stability. To address these issues, we propose a reliable band selection method for anomaly detection. Concretely, we conduct a convolution–transformer hybrid autoencoder architecture to fully exploit the local and global spatial–spectral interdependencies. Next, we design an anomaly–background separability constraint to seamlessly integrate the task priors of anomaly detection into network optimization. Furthermore, we design a spectral attention module to quantify the contribution of different bands during network optimization. Simultaneously, an adaptive band allocation method is designed to optimize the internal structure of the selected band subset. Extensive experiments demonstrate that the proposed method achieves more robust band selection results compared to existing related methods. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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38 pages, 5322 KB

Open AccessSystematic Review

Retrieval of Multiple Variables from Hyperspectral Data: A PRISMA-Aligned Systematic Review of Classical Physics-Based Machine Learning and Hybrid Algorithms in Vegetation and Raw Materials Application Domains

by Andrea Taramelli, Sara Liburdi, Alessandra Nguyen Xuan, Simone Mancon, Serena Sapio and Emiliana Valentini

Remote Sens. 2026, 18(5), 798; https://doi.org/10.3390/rs18050798 - 5 Mar 2026

Viewed by 478

Abstract

Hyperspectral (HSI) remote sensing has emerged as a transformative technology for Earth Observation, enabling detailed assessments across different domains. The current PRISMA-aligned systematic review aims to compare classical physics-based algorithms with emerging machine learning (ML), deep learning (DL) and hybrid approaches across two relevant application domains (vegetation and raw materials), analyzing over 350 peer-reviewed studies (194 after the screening) sourced from Scopus and Web of Science and accessed in July 2025. Specific domain-related studies have been considered, excluding duplicates and studies not strictly related to HSI. Risk of bias was assessed qualitatively based on different criteria. The efficiency of the techniques was analyzed by comparing the accuracy metrics reported in the studies. The heterogeneity of the evaluation metrics used across the different categories of the studies and the underrepresentation of some application domains is the final baseline of the work. The results were synthesized, grouping by application domains and algorithm category: ML and DL models dominate vegetation applications, and physics-based methods remain prevalent in raw materials. Hybrid models achieve the highest performances across all domains. This review highlights the importance of the hyperspectral operational requirements identified for upcoming missions (CHIME, SBG and IRIDE) and points out the opportunity for algorithm development. Full article

(This article belongs to the Special Issue Recent Advances in Hyperspectral Remote Sensing: Theories, Technologies and Applications)

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