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Innovations in Hyperspectral Image Processing: Advancing Image Generation, Denoising, Fusion Techniques and Beyond

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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: closed (28 June 2026) | Viewed by 6814

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Editors

Dr. Jiangjun Peng

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Guest Editor

School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710060, China
Interests: machine learning; hyperspectral image processing; tensor and matrix decomposition

Dr. Xiangyong Cao

E-Mail Website
Guest Editor

School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
Interests: hyperspectral images; remote sensing image processing; machine learning; computer vision; large language models
Special Issues, Collections and Topics in MDPI journals

Dr. Shuang Xu

E-Mail Website
Guest Editor

School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: deep learning; remote sensing image restoration; fusion and interpretation
Special Issues, Collections and Topics in MDPI journals

Dr. Jing Yao

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing; machine learning; deep learning; image processing
Special Issues, Collections and Topics in MDPI journals

Dr. Gemine Vivone

E-Mail Website
Guest Editor

Institute of Methodologies for Environmental Analysis, CNR-IMAA, Tito Scalo, 85050 Tito, Italy
Interests: image fusion; statistical signal processing; deep learning; classification and tracking of remotely sensed images
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Special Issue Information

Dear Colleagues，

Hyperspectral imaging (HSI) can capture hundreds of narrowband spectral responses, providing richer information than traditional imaging. Its applications include remote sensing, agriculture, and target identification and detection. However, hyperspectral data are usually noisy and have a low resolution. In some extreme cases, data are scarce, which limits the accuracy that can be achieved in subsequent tasks. Therefore, obtaining high-quality hyperspectral data is key to subsequent applications.

Recovering clean data from degraded observations is a classic inverse problem, relying heavily on prior knowledge. Hyperspectral data inherently contain rich spectral and image information. Over the recent two decades, numerous statistical regularization-based models for hyperspectral restoration have emerged, offering good interpretability and transferability. However, such models struggle to capture the data's rich structural and texture features. Meanwhile, deep learning has shown strong feature extraction capabilities and effectiveness in restoration tasks but often faces generalization issues. Thus, effectively integrating model-based approaches and data-driven techniques is key for hyperspectral restoration.

This Special Issue aims to identify innovative research that provides deep insights into HSI processing and to provide a community platform for related scholars to share ideas. Contributions that advance hyperspectral image processing are warmly welcomed for submission to this Special Issue. Articles may cover, but are not limited to, the following subjects:

Statistical regularization models for hyperspectral restoration;
Deep learning for hyperspectral image quality enhancement and interpretation, including denoising, super-resolution, anomaly detection, change detection, classification, and so on;
Multi-scale and multi-modal fusion methods;
Unsupervised and semi-supervised restoration approaches;
Hybrid models combining deep learning and statistical regularization;
Large-scale models for hyperspectral restoration;
Hyperspectral image generation;
Remote sensing foundation model;
Reviews/surveys on recent techniques and applications.

Dr. Jiangjun Peng
Dr. Xiangyong Cao
Dr. Shuang Xu
Dr. Jing Yao
Dr. Gemine Vivone
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 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-anonymized 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

hyperspectral image denoising
hyperspectral image fusion
hyperspectral image generation
hyperspectral image restoration
deep learning
statistical regularization model

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Published Papers (6 papers)

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Research

33 pages, 35843 KB

Open AccessArticle

MambaHSINet: A Dual-Branch Bidirectional State Space Network for Hyperspectral Tree Species Classification

by Xinying Liu, Yanfeng Zhang, Junyang Wu, Tianyu Cai, Yumeng Li, Xinran Wang and Xinwei Li

Remote Sens. 2026, 18(14), 2368; https://doi.org/10.3390/rs18142368 - 16 Jul 2026

Viewed by 172

Abstract

Hyperspectral remote sensing provides rich spectral information and has been widely used in fine-grained land-cover classification and forest monitoring. However, accurate tree species classification remains challenging due to subtle interspecific spectral differences, similar spatial structures among related species, redundant spectral bands, and the limited ability of existing methods to model long-range spatial–spectral dependencies efficiently. In addition, many existing hyperspectral image classification methods rely on patch-based inputs and sliding-window inference, which often lead to redundant computation and insufficient utilization of global image context. To address these issues, this paper proposes MambaHSINet, a dual-branch bidirectional state space network for full-image pixel-wise hyperspectral classification. Specifically, the proposed network employs a spectral branch and a spatial branch to explicitly extract complementary spectral responses and spatial structural features. Subsequently, a bidirectional Mamba global modeling module based on selective state space modeling is adopted to capture long-range contextual dependencies in both forward and backward directions with linear computational complexity. Unlike conventional patch-based methods, MambaHSINet takes the entire hyperspectral image as input and produces full-resolution pixel-wise classification maps, thereby avoiding repeated cropping and redundant sliding-window inference. We also construct two well-annotated subsets of a UAV-borne hyperspectral dataset dedicated to tree species classification. Experimental results on self-collected and public hyperspectral datasets demonstrate that the proposed method achieves excellent classification accuracy, inference efficiency, and generalization performance. It exhibits great potential for practical tree species classification and other general hyperspectral application scenarios. Full article

(This article belongs to the Special Issue Innovations in Hyperspectral Image Processing: Advancing Image Generation, Denoising, Fusion Techniques and Beyond)

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28 pages, 68855 KB

Open AccessArticle

Joint Hyperspectral Image Deconvolution and Unmixing via Plug-and-Play Priors

by Sina Layazali and Chrysanthe Preza

Remote Sens. 2026, 18(13), 2066; https://doi.org/10.3390/rs18132066 - 23 Jun 2026

Viewed by 243

Abstract

Hyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations. Addressing these degradations jointly—rather than sequentially—has been shown to improve physical interpretability, yet existing joint deblurring–unmixing methods rely primarily on hand-crafted regularizers that do not fully exploit spatial–spectral structure. Meanwhile, recent plug-and-play (PnP) approaches applied to HSI leverage deep priors but focus solely on either deconvolution or unmixing in isolation. To bridge this gap, we formulate the joint inverse problem of hyperspectral deblurring and spectral unmixing and propose, to our knowledge, the first plug-and-play framework tailored for this coupled task using the Alternating Direction Method of Multipliers (ADMM) and a pretrained deep denoiser (DnCNN) as an implicit PnP prior. Our method uses the natural splitting properties of ADMM to separate a physics-driven subproblem that enforces fidelity to the hyperspectral forward model, which includes linear mixing and blur under a linear, space-invariant convolution approximation, from the data-driven prior step. This synergy of model-based fidelity and learned spatial prior enables more accurate abundance estimates than those obtained with approaches relying solely on analytical regularizers. Experimental results on real hyperspectral datasets demonstrate that the proposed Plug-and-Play Joint Deconvolution and Unmixing (PnP-JDU) method outperforms conventional unmixing baselines, stand-alone PnP unmixing methods, and the Deblurring and Sparse Unmixing via the Alternating Direction Method with Total Variation (DSUnADM-TV) baseline in reconstruction and abundance accuracy metrics. Across the tested datasets and imaging conditions, PnP-JDU achieves lower RMSE, higher PSNR, lower reconstruction and abundance errors, and lower SAD values, while preserving fine spatial details and producing physically meaningful abundance maps. Full article

(This article belongs to the Special Issue Innovations in Hyperspectral Image Processing: Advancing Image Generation, Denoising, Fusion Techniques and Beyond)

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33 pages, 45331 KB

Open AccessArticle

Hyperspectral and Multispectral Image Fusion Based on Adaptive Wavelet Transform and Dual Spectral–Spatial Branch

by Yanhui Chang, Zhiyun Xiao, Jiayang Lu, Tao Fang and Tengfei Bao

Remote Sens. 2026, 18(11), 1726; https://doi.org/10.3390/rs18111726 - 27 May 2026

Viewed by 431

Abstract

As the role of remote sensing continues to grow, the fusion technology of low-spatial-resolution hyperspectral images and high-spatial-resolution multispectral images has become increasingly critical. Traditional methods rely on fixed rules and exhibit poor robustness, whereas deep learning methods struggle to establish efficient interactions between local and global information due to the complexity of their underlying networks. Therefore, we propose a deep learning fusion module that combines pixel-wise adaptive wavelet transform with a spectral–spatial dual-branch extraction. Firstly, by utilizing the unique properties of the wavelet transform, it is possible to effectively preserve spectral information and extract spatial edge features, thereby achieving preliminary fusion by leveraging both low-frequency and high-frequency components. To compensate for the lack of nonlinear expression capability in the wavelet transform, a dual-branch parallel extraction of spectral and spatial features is subsequently performed in the deep learning module. The Multi-Scale Group Convolution module (MSGC) is utilized to extract spectral information, while the Spectral Compression and Spatially Guided Gating Module (SCSGM) is employed to extract spatial information, thereby enhancing the data’s adaptive capability. A bidirectional attention mechanism is interspersed within the module to capture complementary information across different scales, ultimately reconstructing a high-resolution hyperspectral image. Finally, the proposed fusion strategy demonstrates superior performance in practical image reconstruction, outperforming more than ten state-of-the-art fusion methods. Full article

(This article belongs to the Special Issue Innovations in Hyperspectral Image Processing: Advancing Image Generation, Denoising, Fusion Techniques and Beyond)

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23 pages, 5969 KB

Open AccessArticle

A Pyramid-Enhanced Swin Transformer for Robust Hyperspectral–Multispectral Image Fusion and Super-Resolution

by Yu Lu, Lin Hu, Jiankai Hu, Shu Gan, Xiping Yuan, Wang Li and Hailong Zhao

Remote Sens. 2026, 18(8), 1255; https://doi.org/10.3390/rs18081255 - 21 Apr 2026

Viewed by 482

Abstract

Due to the inherent limitations of both hyperspectral and multispectral imagery, balancing high spatial resolution with high spectral fidelity has become one of the fundamental challenges in remote sensing image processing. A prevailing strategy is to fuse these two types of data to reconstruct images that jointly preserve their respective advantages. However, existing reconstruction approaches still suffer from complex coupling between spatial and spectral information, and limited feature extraction capabilities. To address these issues, this study proposes PMSwinNet (Pyramid Multi-scale Swin Transformer Network), a novel architecture that integrates pyramid-based feature enhancement with Transformer mechanisms. The PMSwinNet incorporates multi-scale pyramid feature fusion and window-based self-attention. Through a progressive multi-stage design and three complementary components—feature extraction and reconstruction modules—the Transformer branch leverages window partitioning and shifting operations to capture long-range spatial dependencies and local contextual cues, while the pyramid features extract both global and local information across multiple spatial scales. In addition, a high-frequency branch is introduced, which employs lightweight convolutions to enhance edges, textures, and other high-frequency details, effectively suppressing blurring and artifacts during reconstruction. Experimental evaluations on multiple public hyperspectral datasets demonstrate that the PMSwinNet outperforms state-of-the-art methods, particularly in terms of detail preservation, spectral distortion suppression, and robustness. Full article

(This article belongs to the Special Issue Innovations in Hyperspectral Image Processing: Advancing Image Generation, Denoising, Fusion Techniques and Beyond)

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29 pages, 6701 KB

Open AccessArticle

IFADiff: Training-Free Hyperspectral Image Generation via Integer–Fractional Alternating Diffusion Sampling

by Yang Yang, Xixi Jia, Wenyang Wei, Wenhang Song, Hailong Zhu and Zhe Jiao

Remote Sens. 2025, 17(23), 3867; https://doi.org/10.3390/rs17233867 - 28 Nov 2025

Viewed by 989

Abstract

Hyperspectral images (HSIs) provide rich spectral–spatial information and support applications in remote sensing, agriculture, and medicine, yet their development is hindered by data scarcity and costly acquisition. Diffusion models have enabled synthetic HSI generation, but conventional integer-order solvers such as Denoising Diffusion Implicit Models (DDIM) and Pseudo Linear Multi-Step method (PLMS) require many steps and rely mainly on local information, causing error accumulation, spectral distortion, and inefficiency. To address these challenges, we propose Integer–Fractional Alternating Diffusion Sampling (IFADiff), a training-free inference-stage enhancement method based on an integer–fractional alternating time-stepping strategy. IFADiff combines integer-order prediction, which provides stable progression, with fractional-order correction that incorporates historical states through decaying weights to capture long-range dependencies and enhance spatial detail. This design suppresses noise accumulation, reduces spectral drift, and preserves texture fidelity. Experiments on hyperspectral synthesis datasets show that IFADiff consistently improves both reference-based and no-reference metrics across solvers without retraining. Ablation studies further demonstrate that the fractional order

α

acts as a controllable parameter: larger values enhance fine-grained textures, whereas smaller values yield smoother results. Overall, IFADiff provides an efficient, generalizable, and controllable framework for high-quality HSI generation, with strong potential for large-scale and real-time applications. Full article

(This article belongs to the Special Issue Innovations in Hyperspectral Image Processing: Advancing Image Generation, Denoising, Fusion Techniques and Beyond)

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18 pages, 7888 KB

Open AccessArticle

Hyperspectral Image Denoising Based on Non-Convex Correlated Total Variation

by Junjie Sun, Congwei Mao, Yan Yang, Shengkang Wang and Shuang Xu

Remote Sens. 2025, 17(12), 2024; https://doi.org/10.3390/rs17122024 - 12 Jun 2025

Cited by 4 | Viewed by 3043

Abstract

Hyperspectral image (HSI) quality is generally degraded by diverse noise contamination during acquisition, which adversely impacts subsequent processing performance. Current techniques predominantly rely on nuclear norms and low-rank matrix approximation theory to model the inherent property that HSIs lie in a low-dimensional subspace. Recent research has demonstrated that HSI gradient maps also exhibit low-rank priors. The correlated total variation (CTV), which is defined as the nuclear norm of gradient maps, can simultaneously model low-rank and local smoothness priors, and shows better performance than the standard nuclear norm. However, similar to nuclear norms, CTV may excessively penalize large singular values. To overcome these constraints, this study introduces a non-convex correlated total variation (NCTV), which shows the potential to eliminate mixed noise (including Gaussian, impulse, stripe, and dead-line noise) while preserving critical textures and spatial–spectral details. Numerical experiments on both simulated and real HSI datasets demonstrate that the proposed NCTV method achieves better performance in detail retention compared with the state-of-the-art techniques. Full article

(This article belongs to the Special Issue Innovations in Hyperspectral Image Processing: Advancing Image Generation, Denoising, Fusion Techniques and Beyond)

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