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

Journal Menu

Journal Browser

► Journal Browser

Advanced Algorithms and Techniques for Remote Sensing Image Processing

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

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

Deadline for manuscript submissions: closed (31 May 2026) | Viewed by 10114

Share This Special Issue

Editors

Dr. Yuting Wan

E-Mail Website
Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: real-time remote sensing; neural architecture search; disaster emergency response; unmanned aerial vehicle remote sensing; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Dr. Yang Chen

E-Mail Website
Guest Editor

Beidou Research Institute, South China Normal University, Guangzhou 510631, China
Interests: satellite remote sensing; big data mining; artificial intelligence; spatiotemporal data analysis; urban sustainability
Special Issues, Collections and Topics in MDPI journals

Dr. Junjue Wang

E-Mail Website
Guest Editor

Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan
Interests: multi-modal remote sensing processing; earth vision; land-cover mapping; sustainable urban planning; disaster assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing image processing has become a cornerstone in the field of disaster management, environmental monitoring, urban planning, and a wide range of scientific applications. Recent advancements in remote sensing technologies, including satellite, UAV, and ground-based sensors, have drastically increased the volume and variety of data available for analysis. This has prompted the development of more advanced algorithms and techniques that aim to extract meaningful information from remote sensing imagery, thus providing real-time and accurate remote sensing application services.

In particular, the evolution of machine learning and deep learning techniques has enhanced the processing, analysis, and interpretation of remote sensing data. From automated land cover classification to object detection, image segmentation, and change detection, these advancements offer unprecedented accuracy and scalability in a wide array of applications. Moreover, the integration of multi-source data, such as multispectral, hyperspectral, and radar imagery, has further enriched the information that can be derived from remote sensing data. Furthermore, the development of advanced techniques cannot be separated from the applicative demand, and interdisciplinary applications are equally important.

This Special Issue welcomes contributions that present innovative algorithms, models, and techniques in remote sensing image processing and multidisciplinary studies. The scope of this Special Issue includes, but is not limited to, the following topics:

Multi-modal data registration;
Multi-modal data fusion (e.g., multispectral, hyperspectral, radar);
Advanced classification/segmentation methods;
Advanced object detection methods;
Advanced change detection methods;
Real-time remote sensing image processing;
Applications in disaster emergency assessment;
Applications in urban management;
Applications in ecological risk assessment.

We welcome research articles, review articles, and short communications that address the challenges and opportunities associated with the development of remote sensing image processing techniques. Contributions may focus on new algorithmic developments, novel applications, or case studies showcasing the potential of remote sensing in addressing complex environmental and societal issues.

Dr. Yuting Wan
Dr. Yang Chen
Dr. Junjue 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 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

remote sensing
multi-modal
deep learning
semantic segmentation
object detection
change detection
real-time processing
disaster emergency assessment
urban management and sustainable development
ecological risk assessment

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.

Related Special Issue

Advanced Algorithms and Techniques for Remote Sensing Image Processing (Second Edition) in Remote Sensing

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, 1371 KB

Open AccessArticle

A Weighted Image-Point-Measurement Method of Laser Altimetry Points for Improving Laser-Altimetry-Data-Assisted Positioning Accuracy of Small-Satellite Images

by Wenping Song, Ducheng Wu, Luyao Wang, Miao Li, Jie Han, Caitong Cai, Yang Wu, Fen Tang and Lei Wu

Remote Sens. 2026, 18(13), 2154; https://doi.org/10.3390/rs18132154 - 2 Jul 2026

Viewed by 323

Abstract

For small-satellite imagery, traditional image points derived from the direct back-projection of laser altimetry points onto the imagery often exhibit deviations of tens of pixels or more, owing to the relatively limited attitude and orbit determination accuracy of small-satellite platforms. Moreover, the diversity of imaging sensors, variations in image resolution, and inherently weak image geometric configurations further complicate the accurate acquisition of image-space coordinates for laser altimetry points. To facilitate the application of laser altimetry data for geometric positioning across multi-satellite, multi-sensor, and multi-resolution small-satellite imagery, this study proposes a measurement method for laser altimetry points tailored to small-satellite images and establishes a combined geometric positioning model that integrates virtual control points, laser altimetry points, and image-matching tie points. The framework comprises four key procedural components: (1) an image-point-measurement strategy for laser altimetry points; (2) the construction of a laser altimetry data-assisted geometric positioning model for small-satellite imagery; (3) the solution of the geometric positioning model using a total least squares approach based on the partial-EIV (errors-in-variables) models; and (4) a comprehensive accuracy assessment conducted under multiple image-combination scenarios, including single-satellite single-stereo, single-satellite multi-stereo, dual-satellite single-stereo, and multi-satellite multi-stereo imagery configurations. Experimental validation is carried out using Jilin-1 small-satellite panchromatic images (KF01A, GF02A, and GF02B) acquired over the Henan region of China. The experimental results demonstrate that, with the laser altimetry point-measurement method and the combined geometric positioning model, the vertical positioning accuracy is substantially improved across all tested image-combination scenarios. These findings further confirm the capability in enhancing the vertical geometric positioning performance of stereoscopic small-satellite imagery characterized by multi-satellite platforms, multi-sensors, and multi-resolutions over terrain conditions similar to those tested. Full article

(This article belongs to the Special Issue Advanced Algorithms and Techniques for Remote Sensing Image Processing)

► Show Figures

Figure 1

24 pages, 6617 KB

Open AccessArticle

An Open and Transferable Deep Learning Framework for Mapping Urban Tree Canopy Using NAIP Imagery

by Jooyoung Yoo, Yi Qi, Isaac Ashe-McNalley, Beau MacDonald and John P. Wilson

Remote Sens. 2026, 18(12), 1899; https://doi.org/10.3390/rs18121899 - 9 Jun 2026

Viewed by 941

Abstract

The urban tree canopy is an important resource that spans public and private property and whose form and quantity change over short distances. Although remote sensing and deep learning approaches have been used to map urban tree canopy, the high cost of commercial imagery and the technical complexity of model development have limited their adoption by urban forestry practitioners. We developed a structured and reproducible deep learning workflow optimized for freely available USDA National Agriculture Imagery Program (NAIP) imagery. The workflow incorporates a reproducible U-Net segmentation model for canopy delineation and a YOLOv9e object detection model for individual tree identification, enabling complementary estimation of the canopy extent and individual tree locations. Across two neighborhoods in Los Angeles, the optimized U-Net achieved a Dice coefficient of 0.824 for canopy segmentation, while YOLOv9e reached an F1-score of 0.687 for individual tree detection on a held-out test set with 17,466 annotated trees. A data sufficiency experiment showed that model performance stabilizes when approximately 130 trees are annotated per 320 × 320 pixel (px) tile, corresponding to about 25,379 training and 2641 validation labels, providing a practical target for annotation effort. Additional experiments demonstrate a structured workflow for spatial sampling, training data requirements, and the use of model inferences to estimate tree canopy extent and individual tree locations. The workflow also shows encouraging evidence of transferability to previously unseen urban areas without retraining. By relying solely on NAIP-optimized approaches, this new workflow bridges the gap between complex deep learning techniques and the practical needs of urban foresters; empowers local stakeholders to create accurate, affordable, and timely urban tree inventories; and fosters data-driven decision-making for the sustainable management of urban green infrastructure. Full article

(This article belongs to the Special Issue Advanced Algorithms and Techniques for Remote Sensing Image Processing)

► Show Figures

Figure 1

23 pages, 5270 KB

Open AccessArticle

An Optimized Algorithm for Transmission Line Anomaly Detection Based on Improved YOLOv11n

by Jingpan Bai, Yan Shi, Yuan Chen and Houling Ji

Remote Sens. 2026, 18(12), 1873; https://doi.org/10.3390/rs18121873 - 6 Jun 2026

Viewed by 389

Abstract

To address the issues of low accuracy in transmission line anomaly detection and recognition caused by challenges such as multi-scale targets and partial occlusion, this paper proposes an optimized transmission line anomaly detection algorithm based on improved YOLOv11n. Firstly, an improved Cross Stage Partial with kernel size 2 (C3k2_DFF) module takes the place of the original C3k2 module in the backbone network. It adaptively fuses multi-scale local features and dynamically selects salient channel-wise and spatial features according to its global information during fusion to enhance the model’s feature representation. Secondly, a Separated and Enhancement Attention Module (SEAM) attention mechanism is introduced to enhance the unoccluded area feature response to compensate for the occluded area response deficit, suppressing the background features that interfere with the model and improving the model’s occluded target perception capability. Experimental results on our self-constructed dataset indicate that the proposed improved YOLOv11n model achieves precision, recall, mAP50, and mAP50-95 of 94.2%, 90.8%, 94.3%, and 68.0%, respectively. Compared with the baseline model, it represents improvements of 2.0%, 1.3%, 1.6%, and 2.9%, while the parameters and GFLOPs increase by only 6.6% and 4.8%, demonstrating superior detection performance. Full article

(This article belongs to the Special Issue Advanced Algorithms and Techniques for Remote Sensing Image Processing)

► Show Figures

Figure 1

22 pages, 4940 KB

Open AccessArticle

Enhanced Marine Radar Oil Spill Detection via Feature Guidance and BBO-SA Hybrid Optimization

by Baozhu Jia, Zekun Guo, Jin Xu, Xinru Dong, Lilin Chu, Zheng Li and Haixia Wang

Remote Sens. 2026, 18(10), 1551; https://doi.org/10.3390/rs18101551 - 13 May 2026

Viewed by 477

Abstract

X-band marine radar offers unique advantages for monitoring nearshore oil spills. However, oil films and sea clutter exhibit high pixel intensity overlap in radar images. Traditional threshold segmentation and machine learning methods have certain limitations in terms of feature extraction, Region of Interest (ROI) guidance, threshold optimization adaptability, and unsupervised capabilities. To address these issues, a method of oil film detection for ship radar based on multi-dimensional feature-guided extraction and hybrid optimization search is proposed. By combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering with multidimensional features, this method automatically extracts ROIs under unlabeled conditions, effectively suppressing sea clutter interference. Subsequently, an improved Beaver Behavior Optimizer (BBO) and simulated annealing (SA) hybrid algorithm (BBO-SA) is introduced within the ROIs, along with a designed adaptive temperature update strategy, to achieve coordinated optimization of global and local searches. The experimental results demonstrate that the method described in this paper performs exceptionally well across all evaluation metrics, confirming its accuracy and robustness in oil film detection. It provides a viable technical approach for emergency monitoring of nearshore oil spills. Full article

(This article belongs to the Special Issue Advanced Algorithms and Techniques for Remote Sensing Image Processing)

► Show Figures

Figure 1

25 pages, 3810 KB

Open AccessArticle

DBPINet: A Physics-Informed Inversion Network for Martian Subsurface Radar Signal

by Rui Shi, Liangshuai Guo and Hongxia Ye

Remote Sens. 2026, 18(6), 863; https://doi.org/10.3390/rs18060863 - 11 Mar 2026

Viewed by 888

Abstract

Subsurface exploration of Mars is essential for understanding its geological evolution and potential water ice distribution. Subsurface radar sounding is an effective technique for detecting layered structure and physical parameters beneath the Martian surface. However, existing methods often neglect the influence of loss tangent and rely on data-driven approaches without physical constraints, limiting their accuracy in high-lossy environments and reducing their physical interpretability. To overcome these limitations, this paper proposes a dual-branch physics-informed network (DBPINet) for the joint inversion of layer thickness, permittivity, and loss tangent of Martian layered media. This method introduces a dual signal loss tangent branch (DSLT-Branch) to extract frequency-dependent attenuation features from dual-frequency radar signals and incorporates a physics-informed loss function based on the electromagnetic transmission-line model to embed physical laws into the learning process. Multiple numerical and measured experiments demonstrate the effectiveness of DBPINet. Compared with the MLP-based baseline and the more advanced LMPINet, DBPINet achieves significant improvements in different layered subsurface models. Specifically, on the three-layer models, the mean absolute percentage error (MAPE) for layer thickness, permittivity, and loss tangent is reduced by 4.793%, 3.600% and 4.559%, respectively. Meanwhile, DBPINet exhibits enhanced robustness under noisy conditions. When applied to real Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) data acquired over the Medusae Fossae Formation (MFF) region, the inversion results reveal a three-layer subsurface structure (a volcanic ash surface layer, an ice-mixed basaltic middle layer, and a basaltic basement) that is consistent with existing geological interpretations. Full article

(This article belongs to the Special Issue Advanced Algorithms and Techniques for Remote Sensing Image Processing)

► Show Figures

Figure 1

28 pages, 126976 KB

Open AccessArticle

MRLF: Multi-Resolution Layered Fusion Network for Optical and SAR Images

by Jinwei Wang, Liang Ma, Bo Zhao, Zhenguang Gou, Yingzheng Yin and Guangcai Sun

Remote Sens. 2025, 17(22), 3740; https://doi.org/10.3390/rs17223740 - 17 Nov 2025

Cited by 4 | Viewed by 1544

Abstract

To enhance the comprehensive representation capability and fusion accuracy of remote sensing information, this paper proposes a multi-resolution hierarchical fusion network (MRLF) tailored to the heterogeneous characteristics of optical and synthetic aperture radar (SAR) images. By constructing a hierarchical feature decoupling mechanism, the method decomposes input images into low-resolution global structural features and high-resolution local detail features. A residual compression module is employed to preserve multi-scale information, laying a complementary feature foundation for subsequent fusion. To address cross-modal radiometric discrepancies, a pre-trained complementary feature extraction model (CFEM) is introduced. The brightness distribution differences between SAR and fusion results are quantified using the Gram matrix, and mean-variance alignment constraints are applied to eliminate radiometric discontinuities. In the feature fusion stage, a dual-attention collaborative mechanism is designed, integrating channel attention to dynamically adjust modal weights and spatial attention to focus on complementary regions. Additionally, a learnable radiometric enhancement factor is incorporated to enable efficient collaborative representation of SAR textures and optical semantics. To maintain spatial consistency, hierarchical deconvolution and skip connections are further used to reconstruct low-resolution features, gradually restoring them to the original resolution. Experimental results demonstrate that MRLF significantly outperforms mainstream methods such as DenseFuse and SwinFusion on the Dongying and Xi’an datasets. The fused images achieve an information entropy (EN) of 6.72 and a structural similarity of 1.25, while maintaining stable complementary feature retention under large-scale scenarios. By enhancing multi-scale complementary features and optimizing radiometric consistency, this method provides a highly robust multi-modal representation scheme for all-weather remote sensing monitoring and disaster emergency response. Full article

(This article belongs to the Special Issue Advanced Algorithms and Techniques for Remote Sensing Image Processing)

► Show Figures

Figure 1

20 pages, 47683 KB

Open AccessArticle

Multi-Faceted Adaptive Token Pruning for Efficient Remote Sensing Image Segmentation

by Chuge Zhang and Jian Yao

Remote Sens. 2025, 17(14), 2508; https://doi.org/10.3390/rs17142508 - 18 Jul 2025

Cited by 2 | Viewed by 3836

Abstract

Global context information is essential for semantic segmentation of remote sensing (RS) images. Due to their remarkable capability to capture global context information and model long-range dependencies, vision transformers have demonstrated great performance on semantic segmentation. However, the high computational complexity of vision transformers impedes their broad application in resource-constrained environments for RS image segmentation. To address this challenge, we propose multi-faceted adaptive token pruning (MATP) to reduce computational cost while maintaining relatively high accuracy. MATP is designed to prune well-learned tokens which do not have a close relation to other tokens. To quantify these two metrics, MATP employs multi-faceted scores: entropy, to evaluate the learning progression of tokens; and attention weight, to assess token correlations. Specially, MATP utilizes adaptive criteria for each score that are automatically adjusted based on specific input features. A token is pruned only when both criteria are satisfied. Overall, MATP facilitates the utilization of vision transformers in resource-constrained environments. Experiments conducted on three widely used datasets reveal that MATP reduces the computation cost about 67–70% with about 3–6% accuracy degradation, achieving a superior trade-off between accuracy and computational cost compared to the state of the art. Full article

(This article belongs to the Special Issue Advanced Algorithms and Techniques for Remote Sensing Image Processing)

► Show Figures

Journal Menu

Journal Browser

Advanced Algorithms and Techniques for Remote Sensing Image Processing

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI