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Remote Sensing
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AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land
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AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1929

Special Issue Editors

Dr. Chunqiang Wu

E-Mail Website
Guest Editor
1. CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China
2. State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China
Interests: infrared interferometer data validation and calibration
Dr. Yong Zhang

E-Mail Website
Guest Editor
National Satellite Meteorological Center, Beijing 100081, China
Interests: atmospheric and land remote sensing; remote sensing calibration and validation; environmental science
Special Issues, Collections and Topics in MDPI journals
Dr. Xingfeng Chen

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: quantitatively intelligent remote sensing and atmospheric correction of high resolution imagery
Special Issues, Collections and Topics in MDPI journals
Dr. Chunshan Li

E-Mail Website
Guest Editor
School of Computer and Science, Harbin Institute of Technology, Weihai 264209, China
Interests: remote sensing; multimodal remote sensing; big data computing
Dr. Fu Wang

E-Mail Website
Guest Editor
1. CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China
2. State Key Laboratory of Severe Weather (LaSW), China Meteorological Administration, Beijing 100081, China
Interests: satellite data assimilation; AI for atmospheric application; aerosol–cloud interaction; air pollution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing provides rich spectral information that is critical for understanding atmospheric and land surface processes. Hyperspectral sounders contribute significantly to atmospheric profiling, enabling precise retrieval of temperature, humidity, and trace gases, while hyperspectral imagers are widely used for land surface characterization, including vegetation monitoring, soil analysis, and urban mapping. However, the high dimensionality and complexity of hyperspectral data presents challenges for efficient data processing, noise reduction, and feature extraction.

The emergence of artificial intelligence (AI), particularly deep learning, has revolutionized the analysis of hyperspectral remote sensing data. Deep learning models can efficiently extract complex features, improve retrieval accuracy, and enhance classification and predictive capabilities. By integrating AI-driven techniques with hyperspectral observations, researchers can unlock new possibilities for improving atmospheric and land surface monitoring.

This Special Issue aims to bring together state-of-the-art research on the application of AI, particularly deep learning, in processing and analyzing hyperspectral data for both atmospheric and land studies. It seeks to provide a platform for exploring novel methodologies, theoretical advancements, and practical applications of AI in hyperspectral remote sensing. The scope aligns with the journal’s focus on remote sensing technologies, data analysis, and geospatial applications, emphasizing innovative AI-driven solutions.

We invite original research articles, review papers, and case studies covering, but not limited to, the following topics:

  • AI-based retrieval of atmospheric parameters (e.g., temperature, humidity, gases) from hyperspectral sounders;
  • Deep learning methods for classification and characterization of cloud/aerosol and atmospheric corrections;
  • Hyperspectral land surface applications, including vegetation analysis, soil moisture estimation, and environmental monitoring;
  • AI-based algorithms using multiband and hyperspectral imagers;
  • Data fusion techniques combining hyperspectral sounder and imager data with other remote sensing modalities;
  • Domain adaptation, self-supervised learning, and uncertainty quantification in hyperspectral AI applications.

Dr. Chunqiang Wu
Dr. Yong Zhang
Dr. Xingfeng Chen
Dr. Chunshan Li
Dr. Fu Wang
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

  • deep learning for remote sensing
  • hyperspectral remote sensing
  • retrieval of atmospheric parameter 
  • land surface monitoring hyperspectral sounder and imager applications

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Published Papers (4 papers)

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Research

24 pages, 3937 KiB  
Article
HyperTransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Hyperspectral Image Classification
by Xin Dai, Zexi Li, Lin Li, Shuihua Xue, Xiaohui Huang and Xiaofei Yang
Remote Sens. 2025, 17(14), 2361; https://doi.org/10.3390/rs17142361 - 9 Jul 2025
Viewed by 329
Abstract
Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformers for global dependency modeling. However, existing fusion approaches face three critical challenges: (1) [...] Read more.
Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformers for global dependency modeling. However, existing fusion approaches face three critical challenges: (1) insufficient synergy between spectral and spatial feature learning due to rigid coupling mechanisms; (2) high computational complexity resulting from redundant attention calculations; and (3) limited adaptability to spectral redundancy and noise in small-sample scenarios. To address these limitations, we propose HyperTransXNet, a novel CNN-Transformer hybrid architecture that incorporates adaptive spectral-spatial fusion. Specifically, the proposed HyperTransXNet comprises three key modules: (1) a Hybrid Spatial-Spectral Module (HSSM) that captures the refined local spectral-spatial features and models global spectral correlations by combining depth-wise dynamic convolution with frequency-domain attention; (2) a Mixture-of-Experts Routing (MoE-R) module that adaptively fuses multi-scale features by dynamically selecting optimal experts via Top-K sparse weights; and (3) a Spatial-Spectral Tokens Enhancer (SSTE) module that ensures causality-preserving interactions between spectral bands and spatial contexts. Extensive experiments on the Indian Pines, Houston 2013, and WHU-Hi-LongKou datasets demonstrate the superiority of HyperTransXNet. Full article
(This article belongs to the Special Issue AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land)
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23 pages, 3677 KiB  
Article
HG-Mamba: A Hybrid Geometry-Aware Bidirectional Mamba Network for Hyperspectral Image Classification
by Xiaofei Yang, Jiafeng Yang, Lin Li, Suihua Xue, Haotian Shi, Haojin Tang and Xiaohui Huang
Remote Sens. 2025, 17(13), 2234; https://doi.org/10.3390/rs17132234 - 29 Jun 2025
Viewed by 427
Abstract
Deep learning has demonstrated significant success in hyperspectral image (HSI) classification by effectively leveraging spatial–spectral feature learning. However, current approaches encounter three challenges: (1) high spectral redundancy and the presence of noisy bands, which impair the extraction of discriminative features; (2) limited spatial [...] Read more.
Deep learning has demonstrated significant success in hyperspectral image (HSI) classification by effectively leveraging spatial–spectral feature learning. However, current approaches encounter three challenges: (1) high spectral redundancy and the presence of noisy bands, which impair the extraction of discriminative features; (2) limited spatial receptive fields inherent in convolutional operations; and (3) unidirectional context modeling that inadequately captures bidirectional dependencies in non-causal HSI data. To address these challenges, this paper proposes HG-Mamba, a novel hybrid geometry-aware bidirectional Mamba network for HSI classification. The proposed HG-Mamba synergistically integrates convolutional operations, geometry-aware filtering, and bidirectional state-space models (SSMs) to achieve robust spectral–spatial representation learning. The proposed framework comprises two stages. The first stage, termed spectral compression and discrimination enhancement, employs multi-scale spectral convolutions alongside a spectral bidirectional Mamba (SeBM) module to suppress redundant bands while modeling long-range spectral dependencies. The second stage, designated spatial structure perception and context modeling, incorporates a Gaussian Distance Decay (GDD) mechanism to adaptively reweight spatial neighbors based on geometric distances, coupled with a spatial bidirectional Mamba (SaBM) module for comprehensive global context modeling. The GDD mechanism facilitates boundary-aware feature extraction by prioritizing spatially proximate pixels, while the bidirectional SSMs mitigate unidirectional bias through parallel forward–backward state transitions. Extensiveexperiments on the Indian Pines, Houston2013, and WHU-Hi-LongKou datasets demonstrate the superior performance of HG-Mamba, achieving overall accuracies of 94.91%, 98.41%, and 98.67%, respectively. Full article
(This article belongs to the Special Issue AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land)
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24 pages, 25315 KiB  
Article
PAMFPN: Position-Aware Multi-Kernel Feature Pyramid Network with Adaptive Sparse Attention for Robust Object Detection in Remote Sensing Imagery
by Xiaofei Yang, Suihua Xue, Lin Li, Sihuan Li, Yudong Fang, Xiaofeng Zhang and Xiaohui Huang
Remote Sens. 2025, 17(13), 2213; https://doi.org/10.3390/rs17132213 - 27 Jun 2025
Viewed by 380
Abstract
Deep learning methods have achieved remarkable success in remote sensing object detection. Existing object detection methods focus on integrating convolutional neural networks (CNNs) and Transformer networks to explore local and global representations to improve performance. However, existing methods relying on fixed convolutional kernels [...] Read more.
Deep learning methods have achieved remarkable success in remote sensing object detection. Existing object detection methods focus on integrating convolutional neural networks (CNNs) and Transformer networks to explore local and global representations to improve performance. However, existing methods relying on fixed convolutional kernels and dense global attention mechanisms suffer from computational redundancy and insufficient discriminative feature extraction, particularly for small and rotation-sensitive targets. To address these limitations, we propose a Dynamic Multi-Kernel Position-Aware Feature Pyramid Network (PAMFPN), which integrates adaptive sparse position modeling and multi-kernel dynamic fusion to achieve robust feature representation. Firstly, we design a position-interactive context module (PICM) that incorporates distance-aware sparse attention and dynamic positional encoding. It selectively focuses computation on sparse targets through a decay function that suppresses background noise while enhancing spatial correlations of critical regions. Secondly, we design a dual-kernel adaptive fusion (DKAF) architecture by combining region-sensitive attention (RSA) and reconfigurable context aggregation (RCA). RSA employs orthogonal large-kernel convolutions to capture anisotropic spatial features for arbitrarily oriented targets, while RCA dynamically adjusts the kernel scales based on content complexity, effectively addressing scale variations and intraclass diversity. Extensive experiments on three benchmark datasets (DOTA-v1.0, SSDD, HWPUVHR-10) demonstrate the effectiveness and versatility of the proposed PAMFPN. This work bridges the gap between efficient computation and robust feature fusion in remote sensing detection, offering a universal solution for real-world applications. Full article
(This article belongs to the Special Issue AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land)
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24 pages, 4434 KiB  
Article
MRFP-Mamba: Multi-Receptive Field Parallel Mamba for Hyperspectral Image Classification
by Xiaofei Yang, Lin Li, Suihua Xue, Sihuan Li, Wanjun Yang, Haojin Tang and Xiaohui Huang
Remote Sens. 2025, 17(13), 2208; https://doi.org/10.3390/rs17132208 - 26 Jun 2025
Viewed by 480
Abstract
Deep learning has achieved remarkable success in hyperspectral image (HSI) classification, attributed to its powerful feature extraction capabilities. However, existing methods face several challenges: Convolutional Neural Networks (CNNs) are limited in modeling long-range spectral dependencies because of their limited receptive fields; Transformers are [...] Read more.
Deep learning has achieved remarkable success in hyperspectral image (HSI) classification, attributed to its powerful feature extraction capabilities. However, existing methods face several challenges: Convolutional Neural Networks (CNNs) are limited in modeling long-range spectral dependencies because of their limited receptive fields; Transformers are constrained by their quadratic computational complexity; and Mamba-based methods fail to fully exploit spatial–spectral interactions when handling high-dimensional HSI data. To address these limitations, we propose MRFP-Mamba, a novel Multi-Receptive-Field Parallel Mamba architecture that integrates hierarchical spatial feature extraction with efficient modeling of spatial–spectral dependencies. The proposed MRFP-Mamba introduces two key innovation modules: (1) A multi-receptive-field convolutional module employing parallel 1×1, 3×3, 5×5, and 7×7 kernels to capture fine-to-coarse spatial features, thereby improving discriminability for multi-scale objects; and (2) a parameter-optimized Vision Mamba branch that models global spatial–spectral relationships through structured state space mechanisms. Experimental results demonstrate that the proposed MRFP-Mamba consistently surpasses existing CNN-, Transformer-, and state space model (SSM)-based approaches across four widely used hyperspectral image (HSI) benchmark datasets: PaviaU, Indian Pines, Houston 2013, and WHU-Hi-LongKou. Compared with MambaHSI, our MRFP-Mamba achieves improvements in Overall Accuracy (OA) by 0.69%, 0.30%, 0.40%, and 0.97%, respectively, thereby validating its superior classification capability and robustness. Full article
(This article belongs to the Special Issue AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land)
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