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Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4367

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Editors

Dr. Peixian Zhuang

E-Mail Website
Guest Editor

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: hyperspectral images; deep learning; machine learning; image processing; image enhancement
Special Issues, Collections and Topics in MDPI journals

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

Prof. Dr. Xueyang Fu

E-Mail Website
Guest Editor

Department of Automation, School of Automation, University of Science and Technology of China, Hefei 230026, China
Interests: machine learning; low-level image processing; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral imaging (HSI), with its ability to capture detailed spectral information across numerous contiguous bands, has revolutionized remote sensing by enabling fine-grained material identification and analysis. From precision agriculture and environmental monitoring to mineral exploration and urban planning, the applications of HSI are vast and critical. However, the high dimensionality, spectral–spatial complexity, and inherent noise of hyperspectral data present significant challenges for traditional analytical methods. Effectively unlocking the rich information within these datasets requires sophisticated computational approaches. Thus, it is urgent to analyze HSI through the advanced technological approaches of deep learning and computer vision.

This Special Issue aims to showcase the latest breakthroughs at the intersection of hyperspectral image analysis (HSI), advanced deep learning, and computer vision. It explores how modern computational intelligence can overcome the traditional limitations of HSI processing, pushing the boundaries of accuracy, efficiency, and interpretability. The goal is to compile a collection of high-quality research that demonstrates novel methodologies, addresses fundamental challenges like limited labeled data and model generalization, and opens new avenues for practical HSI applications.

Articles may address, but are not limited to, the following topics:

Advanced Deep Learning Models for HSI: Exploration of Transformers, Graph Neural Networks (GNNs), Diffusion Models, and Large Language Models (LLMs) for HSI classification, segmentation, and target detection.
Self-Supervised, Semi-Supervised, and Unsupervised Learning: Innovative techniques to mitigate the challenge of limited ground-truth labels in HSI analysis.
Spectral–Spatial Feature Fusion: Novel architectures and methods (e.g., Pansharpening) for joint and effective exploitation of spectral and spatial information.
HSI Super-Resolution and Restoration: Enhancing spatial and spectral resolution using deep learning, and developing related techniques for HSI denoising and destriping.
Explainable AI (XAI) for HSI Interpretation: Developing transparent and interpretable deep learning models (such as deep unfolding networks, white-box Transformer, etc.) to build trust and provide insights into model decisions.
Domain Adaptation and Transfer Learning: Methods to improve deep learning model robustness and generalizability across different sensors, seasons, and geographical areas.
Lightweight and Efficient Deep Learning Models: Solutions for real-time HSI processing onboard satellites, drones, and other mobile platforms.
Multimodal Data Fusion: Integrating HSI with LiDAR, SAR, or other data sources using deep learning for comprehensive analysis.
Generative Models for HSI: Using GANs or VAEs for data augmentation, synthesis, anomaly detection, etc.

Dr. Peixian Zhuang
Dr. Xiangyong Cao
Prof. Dr. Xueyang Fu
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 analysis
spectral–spatial feature fusion
self-supervised learning
Explainable AI (XAI)
domain adaptation
multimodal data fusion
generative models
lightweight deep learning
transformer networks
large language models

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Published Papers (6 papers)

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Research

22 pages, 65601 KB

Open AccessArticle

Dual-Domain Illumination Prior for Low-Light Remote Sensing Image Enhancement

by Chao Wang, Zhe Pan, Liangtian He, Jun Liu, Lin Mei, Rongsheng Lin, Hongming Chen and Chuansheng Yang

Remote Sens. 2026, 18(16), 2817; https://doi.org/10.3390/rs18162817 - 20 Aug 2026

Viewed by 313

Abstract

Low-light conditions degrade remote sensing imagery by reducing contrast, distorting color, and obscuring fine terrain structures and small objects critical for Earth observation. Accurate illumination adjustment under spatially varying scene content remains challenging for existing enhancement methods, and many prior-guided approaches operate exclusively in either the spatial domain or the frequency domain. In this work, we propose a Dual-Domain Illumination Prior (DDIP), a trainable dual-domain illumination-prior module that is jointly optimized with each host backbone and exploits frequency-domain and spatial-domain illumination statistics. DDIP comprises three components: a Frequency-Domain Illumination Distribution Prior (FIDP) that performs per-color-channel amplitude calibration in Fourier space to improve global brightness; a Spatial-Domain Illumination Distribution Prior (SIDP), adapted from IDP-Net, that performs multi-scale sub-region statistical correction for local illumination adjustment; and a Selective Core Feature Fusion (SCFF) module that adaptively combines the frequency-domain output, the spatial-domain output, and the original input through an attention-based gating mechanism with dual pooling. DDIP is integrated with each host backbone while leaving its main restoration blocks unchanged. In the controlled reconstruction comparisons on iSAID-dark and the evaluated general low-light benchmarks, equipping the tested backbone networks with DDIP improves PSNR and SSIM over their corresponding baselines. Complementary LPIPS and CIELAB lightness measurements characterize perceptual similarity and lightness behavior, while a fixed-detector object-detection evaluation on the tested high-resolution iSAID-dark scenes examines the effect of the enhancement pipelines under the reported synthetic low-light conditions. The ablation studies further examine the contribution of the module components within the reported experimental settings. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision)

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27 pages, 12627 KB

Open AccessArticle

Confidence-Aware Selective Test-Time Adaptation for Remote-Sensing Pansharpening

by Jiangyun Li, Tong Gu, Xiaochen Zhang, Fuheng Xiao, Yanyu Yin and Peixian Zhuang

Remote Sens. 2026, 18(16), 2801; https://doi.org/10.3390/rs18162801 - 19 Aug 2026

Viewed by 317

Abstract

Pansharpening aims to fuse low-resolution multispectral (LRMS) and panchromatic (PAN) images to generate high-resolution multispectral (HRMS) imagery. However, models trained on data from a specific sensor often generalize poorly to unseen sensors, resulting in significant performance degradation. Existing solutions can be categorized into full model retraining and zero-shot adaptation. The former requires substantial computational resources and labeled target-domain data, making it impractical for rapid deployment. The latter avoids retraining but typically suffers from considerable performance degradation under cross-sensor domain shifts. To address the issues, we propose a Confidence-Aware Selective Test-time Adaptation (CSTTA) framework for cross-sensor pansharpening. Specifically, test-time augmentation is employed to estimate prediction uncertainty and construct confidence-aware weighting maps, which guide adaptation toward more reliable regions. In addition, a multi-patch sensitivity-based parameter selection strategy is introduced to update only a small subset of sensor-sensitive parameters, thereby reducing optimization cost while maintaining adaptation effectiveness. Spatial, spectral, and output-consistency constraints are further incorporated to stabilize the adaptation process and balance structural preservation with spectral fidelity. Extensive experiments on multiple cross-sensor pansharpening benchmarks demonstrate that CSTTA consistently improves the performance of various backbone networks and achieves state-of-the-art results compared with existing transfer methods. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision)

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20 pages, 30488 KB

Open AccessArticle

Hierarchical Scale-Adaptive Diffusion Priors for Efficient Remote Sensing Dehazing

by Wei Ju, Zheng Liang, Huan Chen and Jie Shen

Remote Sens. 2026, 18(12), 1907; https://doi.org/10.3390/rs18121907 - 9 Jun 2026

Viewed by 404

Abstract

Remote sensing image dehazing remains a formidable challenge due to complex atmospheric scattering and large-scale spatially varying degradation, which severely compromise fine-grained surface details. While recent diffusion-based restoration frameworks, such as DiffIR, have achieved remarkable efficiency by injecting compact diffusion priors into deterministic networks, they typically rely on a monolithic global Image Prior Representation (IPR). However, such a global design is suboptimal for the dehazed results of remote sensing imagery, where haze distribution exhibits strong spatial heterogeneity and scale dependency. To address this limitation, this paper presents the Hierarchical and Scale-Adaptive Diffusion Prior (HS-DiffIR) framework. Specifically, Hierarchical Image Prior Representation decomposes the holistic diffusion latent into multi-scale priors aligned with the hierarchical stages of the restoration network. Such a design facilitates fine-grained, scale-aware guidance by projecting the compact global latent into layer-specific representations, thereby bypassing the computational burden of high-dimensional generative modeling. Complementing this, the Scale-Adaptive Injection mechanism utilizes lightweight learnable coefficients to dynamically modulate the influence of diffusion priors across different feature scales, allowing the network to adaptively balance global semantic consistency and local detail recovery under dense-haze conditions. Evaluations on remote sensing benchmarks confirm that HS-DiffIR generally outperforms the DiffIR baseline. The method yields superior quantitative metrics (particularly PSNR) at a marginal computational cost while demonstrating robust detail restoration in regions subject to severe, spatially variant haze. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision)

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27 pages, 72468 KB

Open AccessArticle

Long-Tailed Remote Sensing Image Classification via Multi-Scale Data, Pre-Trained Model, and Efficient Inference Strategy

by Song Han, Xing Han, Yibo Xu, Yongqin Tian, Weidong Zhang and Wenyi Zhao

Remote Sens. 2026, 18(10), 1636; https://doi.org/10.3390/rs18101636 - 19 May 2026

Viewed by 663

Abstract

Remote sensing image classification is one of the fundamental tasks in the field of remote sensing and plays a critical role in Earth observation applications. However, the inherent multi-scale characteristics of this task pose significant challenges to scene classification. To address these issues, we propose a novel framework that integrates the Contrastive Language–Image Pre-training (CLIP) model, multi-scale data, and efficient inference strategy. The proposed framework transfers general-purpose features learnt from natural images to remote sensing image classification. Specifically, this framework leverages the rich feature representations learnt by the CLIP model in the contrastive learning procedure and adopts it as the backbone network of the model to extract fine-grained and multi-scale features for remote sensing images. That is, the model can learn local fine-grained details but also encode global contextual information useful for the classification of visually similar scene categories. Afterwards, AdapterFormer module is inserted into the few selected layers of CLIP model, which can effectively enhance model performance and have low computational overhead. This helps efficient knowledge sharing and introduces new features at the model level. Furthermore, to alleviate possible performance deterioration brought about by multi-scale feature variation, a multi-scale training set is constructed at data level, providing complementary multi-scale information. Through the synergy of all these strategies above, the proposed method greatly improves the classification performance of multi-scale remote sensing images. Extensive experiments on the MEET dataset (it includes 80 fine categories and more than 800,000 samples) show that the proposed method greatly improves the performance. Compared with general-purpose classification networks and remote sensing-related models, the proposed method always gets state-of-the-art results. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision)

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28 pages, 12288 KB

Open AccessArticle

CALCNet: A Novel Cross-Module Attention Network for Efficient Land Cover Classification

by Muhammad Fayaz, Hikmat Yar, Weiwei Jiang, Anwar Hassan Ibrahim, Muhammad Islam and L. Minh Dang

Remote Sens. 2026, 18(8), 1218; https://doi.org/10.3390/rs18081218 - 17 Apr 2026

Viewed by 731

Abstract

Land cover classification (LCC) is a fundamental task in remote sensing, which enables effective environmental monitoring, agricultural planning, and disaster management. The existing approaches often rely on fine-tuning pre-trained models, which are not specifically designed for LCC, which lead to suboptimal performance in complex scenarios. To address these limitations, we propose the Cross-Module Attention Land Cover Network (CALCNet), a novel architecture developed from scratch. CALCNet follows a contracting and restoration backbone, where the contracting path extracts progressively abstract semantic features while reducing spatial resolution, and the restoration path recovers fine-grained spatial details through upsampling and skip connections. In addition, CALCNet integrates a cross-module attention mechanism that combines spatial attention and multi-scale feature selection to enhance feature representation. Furthermore, we applied a differential evolution-based neuron pruning strategy to create a compressed CALCNet variant, which retains high classification performance while reducing computational cost. The CALCNet is evaluated on four benchmark LCC datasets, AID, UCMerced_LandUse, NWPU_RESISC45, and EuroSAT, demonstrating strong performance across all benchmarks. Specifically, the model achieves classification accuracies of 98.09%, 99.47%, 99.19%, and 99.19%, respectively. The compressed CALCNet variant reduces computational cost to 78.55 million floating point operations (FLOPs) with a model size of 43 MB, while achieving improved inference speeds (38.32 frames/sec on CPU and 118.3 frames/sec on GPU), representing approximately 45–50% reduction in FLOPs and model storage. These results highlight that CALCNet is both highly accurate and computationally efficient, making it well suited for real-world LCC applications. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision)

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20 pages, 1738 KB

Open AccessArticle

STAIT: A Spatio-Temporal Alternating Iterative Transformer for Multi-Temporal Remote Sensing Image Cloud Removal

by Yukun Cui, Jiangshe Zhang, Haowen Bai, Zixiang Zhao, Lilun Deng, Shuang Xu and Chunxia Zhang

Remote Sens. 2026, 18(4), 596; https://doi.org/10.3390/rs18040596 - 14 Feb 2026

Cited by 1 | Viewed by 889

Abstract

Multi-temporal remote sensing image cloud removal aims to reconstruct land surface information in regions obscured by clouds and their shadows, thereby mitigating a major constraint on the application of remote sensing imagery. However, existing multi-temporal deep learning methods for cloud removal often fail to model complex spatio-temporal dynamics, leading to suboptimal performance. To address this challenge, we propose a novel framework for multi-temporal cloud removal. In this architecture, the most critical component is the Spatio-Temporal Alternating Iterative Transformer (STAIT), which primarily consists of temporal and spatial attention mechanisms. STAIT is engineered to refine spatio-temporal feature representation by establishing an effective interplay between spatial details and temporal dynamics. Our framework is enhanced by an efficient image token generator with group convolution-based multi-level feature extraction to manage complexity, and a pixel reconstruction decoder with a shared progressive upsampling network to improve reconstruction by learning time-invariant features. Experimental results demonstrate that by explicitly modeling spatio-temporal feature dependencies, our approach achieves superior performance in restoring high-fidelity, cloud-free imagery. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision)

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Hyperspectral Remote Sensing Image Analysis via Advanced Deep Learning and Computer Vision

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