Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 July 2026 | Viewed by 12161

Share This Special Issue

Special Issue Editors

Dr. Haopeng Zhang

E-Mail Website
Guest Editor

School of Astronautics, Beihang University, Beijing 102206, China
Interests: image processing; pattern recognition; computer vision; machine learning; remote sensing; aerospace exploration
Special Issues, Collections and Topics in MDPI journals

Dr. Giorgio Antonino Licciardi

E-Mail Website1 Website2
Guest Editor

Hypatia Research Consortium, Via del Politecnico SNC, C/O Italian Space Agency, 00133 Rome, Italy
Interests: hyperspactral image analysis; machine learning; deep learning techniques; dimensionality reduction; super-resolution; spectral unmixing; data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The processing and automatic interpretation of the extensive remote sensing data obtained by satellites in orbit, near-space vehicles or manned/unmanned aerial vehicles (UAV) has always been a popular topic in the field of remote sensing. In recent years, artificial intelligence (AI) technology, especially deep learning, has developed rapidly and significantly accelerated research in a range of areas. Intelligent processing and analysis have extensive applicative potential in multiple remote sensing data, including panchromatic, multispectral, hyperspectral, infrared, SAR, LiDAR, etc. This Special Issue aims to compile recent works in this field that provide methodological contributions and innovative applications. Novel research that employs intelligent methods to address practical problems in remote sensing applications is also welcome. The scope of this Special Issue includes, but is not limited to, the following topics:

Intelligent image processing for remote sensing, including restoration (denoising, deblurring and dehazing), super-resolution, enhancement, registration, pan-sharpening, etc.;
Unsupervised/weakly-supervised/self-supervised learning for image processing and analysis of remote sensing data;
Data fusion/multi-modal data analysis;
Domain adaption for cross-modal data analysis;
AI methods for image/scene classification;
AI methods for target detection/recognition/tracking;
Cloud detection and removal in remote sensing images;
Change detection/semantic segmentation for remote sensing;
Large models for remote sensing;
Deep learning for remote sensing applications.

Dr. Haopeng Zhang
Dr. Giorgio Licciardi
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-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
image processing
image analysis
artificial intelligence
deep learning
data fusion
multispectral and hyperspectral data
synthetic aperture radar
LiDAR

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

26 pages, 13001 KB

Open AccessArticle

Point-HRRP-Net: A Deep Fusion Framework via Bi-Directional Cross-Attention for Space Object Classification Using HRRP and Point Cloud

by Zhenou Zhao, Zhuoyi Yang, Haitao Zhang, Yanwei Wang and Kuo Meng

Remote Sens. 2026, 18(6), 868; https://doi.org/10.3390/rs18060868 - 11 Mar 2026

Viewed by 350

Abstract

High-Resolution Range Profile (HRRP)-based space object classification is severely limited by aspect sensitivity. Inspired by the intrinsic complementarity between HRRP and LiDAR point clouds, this work investigates the feasibility and effectiveness of fusing these two modalities to address this limitation. We propose the Point-HRRP-Net framework. This framework employs dual-stream extractors to independently encode HRRP electromagnetic signatures and 3D point cloud geometric topologies. Subsequently, a Bi-Directional Cross-Attention (Bi-CA) mechanism is designed to fuse the two modalities. To enable information interaction, this mechanism utilizes point-to-point attention to correlate radar scattering features with 3D geometric points, thereby constructing a comprehensive target representation. Due to data scarcity, we constructed a paired simulation dataset for evaluation. Experimental results demonstrate that the proposed framework consistently outperforms its constituent single-modality baselines. The model achieves 57.67% accuracy on the 180° split and demonstrates generalization capability to unseen viewpoints. Ablation studies further validate the efficacy of the Bi-CA mechanism and the selected feature extractors. Finally, we assess the potential sim-to-real discrepancies and evaluate deployment feasibility across various hardware platforms. Full article

(This article belongs to the Special Issue Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data)

► Show Figures

Figure 1

23 pages, 14239 KB

Open AccessArticle

Dense Representative Points-Guided Rotated-Ship Detection in Remote Sensing Images

by Ning Zhao, Yongfei Xian, Tairan Zhou, Jiawei Shi, Zhiguo Jiang and Haopeng Zhang

Remote Sens. 2026, 18(3), 458; https://doi.org/10.3390/rs18030458 - 1 Feb 2026

Viewed by 439

Abstract

Withcontinuous advancements in remote sensing technology, object detection in remote sensing images has emerged as a critical research direction in maritime surveillance, port management, and national defense. Among these applications, ship detection is a key task. Due to the fact that ships in images typically exhibit arbitrary rotations, multi-scale distributions, and complex backgrounds, conventional detection methods based on horizontal or rotated bounding boxes often fail to adequately capture the fine-grained information of the targets, thereby compromising detection accuracy. This paper proposes the Dense Representative Points-Guided Rotated-Ship Detection (DenseRRSD) method. The proposed approach represents ship objects using dense representative points (RepPoints) to effectively capture local semantic information, thereby avoiding the background noise issues associated with traditional rectangular bounding box representations. To further enhance detection accuracy, an edge region sampling strategy is devised to uniformly sample RepPoints from critical ship parts, and a Weighted Residual Feature Pyramid Network (WRFPN) is introduced to efficiently fuse the multi-scale features through residual connections and learnable weights. In addition, a Weighted Chamfer Loss (WCL) combined with a staged localization loss strategy is employed to progressively refine localization from coarse to fine stages. Experimental results on both the HRSC2016 dataset and the newly constructed DOTA-SHIP dataset demonstrate that DenseRRSD achieves state-of-the-art detection accuracy, with mean Average Precision (mAP) scores of 91.2% and 83.2%, respectively, significantly outperforming existing methods. These results verify the effectiveness and robustness of the proposed approach in rotated-ship detection under diverse conditions. Full article

(This article belongs to the Special Issue Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data)

► Show Figures

Figure 1

25 pages, 4824 KB

Open AccessArticle

SCMT-Net: Spatial Curvature and Motion Temporal Feature Synergy Network for Multi-Frame Infrared Small Target Detection

by Ruiqi Yang, Yuan Liu, Ming Zhu, Huiping Zhu and Yuanfu Yuan

Remote Sens. 2026, 18(2), 215; https://doi.org/10.3390/rs18020215 - 9 Jan 2026

Viewed by 517

Abstract

Infrared small target (IRST) detection remains a challenging task due to extremely small target sizes, low signal-to-noise ratios (SNR), and complex background clutter. Existing methods often fail to balance reliable detection with low false alarm rates due to limited spatial–temporal modeling. To address this, we propose a multi-frame network that synergistically integrates spatial curvature and temporal motion consistency. Specifically, in the single-frame stage, a Gaussian Curvature Attention (GCA) module is introduced to exploit spatial curvature and geometric saliency, enhancing the discriminability of weak targets. In the multi-frame stage, a Motion-Aware Encoding Block (MAEB) utilizes MotionPool3D to capture temporal motion consistency and extract salient motion regions, while a Temporal Consistency Enhancement Module (TCEM) further refines cross-frame features to effectively suppress noise. Extensive experiments demonstrate that the proposed method achieves advanced overall performance. In particular, under low-SNR conditions, the method improves the detection rate by 0.29% while maintaining a low false alarm rate, providing an effective solution for the stable detection of weak and small targets. Full article

(This article belongs to the Special Issue Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data)

► Show Figures

Graphical abstract

19 pages, 9218 KB

Open AccessArticle

A Hybrid ANN–GWR Model for High-Accuracy Precipitation Estimation

by Ye Zhang, Leizhi Wang, Lingjie Li, Yilan Li, Yintang Wang, Xin Su, Xiting Li, Lulu Wang and Fei Yao

Remote Sens. 2025, 17(15), 2610; https://doi.org/10.3390/rs17152610 - 27 Jul 2025

Viewed by 1359

Abstract

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

(This article belongs to the Special Issue Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data)

► Show Figures

Graphical abstract

22 pages, 26552 KB

Open AccessArticle

Res-LK-SLR: A Residual Network Based on Large Kernels and Shapelet-Level Representations for Spatial Infrared Spot Target Discrimination

by Huiying Liu, Jiarong Wang, Weijun Zhong, Haitao Nie, Xiaotong Deng, Jiaqi Sun, Ming Zhu and Ming Wei

Remote Sens. 2024, 16(24), 4624; https://doi.org/10.3390/rs16244624 - 10 Dec 2024

Viewed by 1288

Abstract

Spatial infrared spot target (SIST) discrimination based on infrared radiation sequences (IRSs) can be considered a univariate trending time series classification task. However, due to the complexity of actual scenarios and the limited opportunities for acquiring IRSs, resulting in noise interference, extremely small-scale datasets with imbalanced distribution of classes and widely varying sequence lengths range from a few hundred to several thousand time steps. Current research is primarily based on idealized simulation datasets, resulting in a performance gap when applied to actual applications. To address these issues, firstly, we construct a simulation dataset tailored to the challenges of actual scenarios. Secondly, we design a practical data preprocessing method to achieve uniform sequence length, coarse alignment of shapelets and filtering while preserving key points. Thirdly, we propose a residual network Res-LK-SLR for IRS classification based on large kernels (LKs, providing long-term dependence) and shapelet-level representations (SLRs, where the hidden layer features are aligned with the learned high-level representations to obtain the optimal segmentation and generate shapelet-level representations). Additionally, we conduct extensive evaluations and validations on both the simulation dataset and 18 UCR time series classification datasets. The results demonstrate the effectiveness and generalization ability of our proposed Res-LK-SLR. Full article

(This article belongs to the Special Issue Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data)

► Show Figures

Figure 1

22 pages, 3682 KB

Open AccessArticle

MDFA-Net: Multi-Scale Differential Feature Self-Attention Network for Building Change Detection in Remote Sensing Images

by Yuanling Li, Shengyuan Zou, Tianzhong Zhao and Xiaohui Su

Remote Sens. 2024, 16(18), 3466; https://doi.org/10.3390/rs16183466 - 18 Sep 2024

Cited by 3 | Viewed by 2670

Abstract

Building change detection (BCD) from remote sensing images is an essential field for urban studies. In this well-developed field, Convolutional Neural Networks (CNNs) and Transformer have been leveraged to empower BCD models in handling multi-scale information. However, it is still challenging to accurately detect subtle changes using current models, which has been the main bottleneck to improving detection accuracy. In this paper, a multi-scale differential feature self-attention network (MDFA-Net) is proposed to effectively integrate CNN and Transformer by balancing the global receptive field from the self-attention mechanism and the local receptive field from convolutions. In MDFA-Net, two innovative modules were designed. Particularly, a hierarchical multi-scale dilated convolution (HMDConv) module was proposed to extract local features with hybrid dilation convolutions, which can ameliorate the effect of CNN’s local bias. In addition, a differential feature self-attention (DFA) module was developed to implement the self-attention mechanism at multi-scale difference feature maps to overcome the problem that local details may be lost in the global receptive field in Transformer. The proposed MDFA-Net achieves state-of-the-art accuracy performance in comparison with related works, e.g., USSFC-Net, in three open datasets: WHU-CD, CDD-CD, and LEVIR-CD. Based on the experimental results, MDFA-Net significantly exceeds other models in F1 score, IoU, and overall accuracy; the F1 score is 93.81%, 95.52%, and 91.21% in WHU-CD, CDD-CD, and LEVIR-CD datasets, respectively. Furthermore, MDFA-Net achieved first or second place in precision and recall in the test in all three datasets, which indicates its better balance in precision and recall than other models. We also found that subtle changes, i.e., small-sized building changes and irregular boundary changes, are better detected thanks to the introduction of HMDConv and DFA. To this end, with its better ability to leverage multi-scale differential information than traditional methods, MDFA-Net provides a novel and effective avenue to integrate CNN and Transformer in BCD. Further studies could focus on improving the model’s insensitivity to hyper-parameters and the model’s generalizability in practical applications. Full article

(This article belongs to the Special Issue Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data)

► Show Figures

Graphical abstract

25 pages, 16150 KB

Open AccessArticle

Multi-Degradation Super-Resolution Reconstruction for Remote Sensing Images with Reconstruction Features-Guided Kernel Correction

by Yi Qin, Haitao Nie, Jiarong Wang, Huiying Liu, Jiaqi Sun, Ming Zhu, Jie Lu and Qi Pan

Remote Sens. 2024, 16(16), 2915; https://doi.org/10.3390/rs16162915 - 9 Aug 2024

Cited by 8 | Viewed by 3290

Abstract

A variety of factors cause a reduction in remote sensing image resolution. Unlike super-resolution (SR) reconstruction methods with single degradation assumption, multi-degradation SR methods aim to learn the degradation kernel from low-resolution (LR) images and reconstruct high-resolution (HR) images more suitable for restoring the resolution of remote sensing images. However, existing multi-degradation SR methods only utilize the given LR images to learn the representation of the degradation kernel. The mismatches between the estimated degradation kernel and the real-world degradation kernel lead to a significant deterioration in performance of these methods. To address this issue, we design a reconstruction features-guided kernel correction SR network (RFKCNext) for multi-degradation SR reconstruction of remote sensing images. Specifically, the proposed network not only utilizes LR images to extract degradation kernel information but also employs features from SR images to correct the estimated degradation kernel, thereby enhancing the accuracy. RFKCNext utilizes the ConvNext Block (CNB) for global feature modeling. It employs CNB as fundamental units to construct the SR reconstruction subnetwork module (SRConvNext) and the reconstruction features-guided kernel correction network (RFGKCorrector). The SRConvNext reconstructs SR images based on the estimated degradation kernel. The RFGKCorrector corrects the estimated degradation kernel by reconstruction features from the generated SR images. The two networks iterate alternately, forming an end-to-end trainable network. More importantly, the SRConvNext utilizes the degradation kernel estimated by the RFGKCorrection for reconstruction, allowing the SRConvNext to perform well even if the degradation kernel deviates from the real-world scenario. In experimental terms, three levels of noise and five Gaussian blur kernels are considered on the NWPU-RESISC45 remote sensing image dataset for synthesizing degraded remote sensing images to train and test. Compared to existing super-resolution methods, the experimental results demonstrate that our proposed approach achieves significant reconstruction advantages in both quantitative and qualitative evaluations. Additionally, the UCMERCED remote sensing dataset and the real-world remote sensing image dataset provided by the “Tianzhi Cup” Artificial Intelligence Challenge are utilized for further testing. Extensive experiments show that our method delivers more visually plausible results, demonstrating the potential of real-world application. Full article

(This article belongs to the Special Issue Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data)

► Show Figures

Journal Menu

Journal Browser

Intelligent Processing and Analysis of Multi-Modal Remote Sensing Data

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI