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Advances in Computer Vision and Machine Learning Applications on Remote Sensing Images

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

Deadline for manuscript submissions: 12 February 2026 | Viewed by 1209

Special Issue Editors

Dr. Qiang Li

E-Mail Website
Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern, Xi'an 710072, China
Interests: remote sensing image processing; image quality enhancement; object/change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Li

E-Mail Website
Guest Editor
Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore, Singapore
Interests: large language model tuning; data privacy; model robustness
Dr. Dan Wang

E-Mail Website
Guest Editor
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
Interests: the intersection of computer vision, machine learning, and computer graphics
Dr. Tania Stathaki

E-Mail Website
Guest Editor
Communications and Signal Processing Research Group, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Interests: image fusion; computer vision; remote sensing; urban monitoring; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technology has become a pivotal tool in various fields, including environmental monitoring, urban planning, disaster management, and agriculture. With the increased availability of high-resolution satellite imagery and aerial photography, the volume and complexity of remote sensing data have skyrocketed, necessitating the development of advanced computational techniques. In recent years, the intersection of computer vision (CV) and machine learning (ML) has shown great potential in improving the accuracy, efficiency, and scalability of remote sensing image analysis. This Special Issue seeks to highlight the latest advances in the application of computer vision and machine learning techniques to remote sensing images, showcasing how these methodologies are transforming the analysis of geospatial data. The Special Issue will provide a platform for researchers, practitioners, and policymakers to understand how these advanced computational techniques are revolutionizing the field of remote sensing and can be leveraged to address global challenges such as climate change, natural disasters, and sustainable development.

The primary objective of this Special Issue is to provide a comprehensive overview of the current state-of-the-art methods and innovations in computer vision and machine learning for remote sensing. We aim to focus on new algorithms, novel applications, and emerging trends in the integration of CV and ML for remote sensing image interpretation. Topics may cover anything from classical detection to tracking, recognition, matching, and classification for remote sensing applications. Hence, multisource data fusion (e.g., multispectral, hyperspectral, and thermal), multiscale approaches, or studies focused on the intersection of CV and ML for remote sensing image interpretation, among other issues, are welcome. Articles may address, but are not limited, to the following topics:

Multimodal and multispectral data fusion;

Image matching;

Image quality enhancement;

Dimensional reduction and clustering;

Geographic information extraction, such as roads, buildings, and water bodies;

Object detection and recognition, change detection, and anomaly detection;

High-fidelity urban 3D modelling and scene simulation;

Unsupervised and semi-supervised learning;

Explainable AI (XAI) in remote sensing;

Real-time processing and edge computing: techniques for the real-time processing of remote sensing data, especially using edge devices and cloud computing platforms;

Applications in specific domains, such as agriculture (crop monitoring, pest detection), environmental management (deforestation, biodiversity), urban planning (city development, traffic monitoring), and disaster response (flood, wildfire detection).

Dr. Qiang Li
Dr. Jing Li
Dr. Dan Wang
Dr. Tania Stathaki
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

  • remote sensing image processing
  • geospatial data analysis
  • computer vision
  • machine learning
  • satellite imagery
  • image fusion
  • advanced computational techniques

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Published Papers (2 papers)

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25 pages, 85368 KiB  
Article
SMA-YOLO: An Improved YOLOv8 Algorithm Based on Parameter-Free Attention Mechanism and Multi-Scale Feature Fusion for Small Object Detection in UAV Images
by Shenming Qu, Chaoxu Dang, Wangyou Chen and Yanhong Liu
Remote Sens. 2025, 17(14), 2421; https://doi.org/10.3390/rs17142421 - 12 Jul 2025
Viewed by 491
Abstract
With special consideration for complex scenes and densely distributed small objects, this frequently leads to serious false and missed detections for unmanned aerial vehicle (UAV) images in small object detection scenarios. Consequently, we propose a UAV image small object detection algorithm, termed SMA-YOLO. [...] Read more.
With special consideration for complex scenes and densely distributed small objects, this frequently leads to serious false and missed detections for unmanned aerial vehicle (UAV) images in small object detection scenarios. Consequently, we propose a UAV image small object detection algorithm, termed SMA-YOLO. Firstly, a parameter-free simple slicing convolution (SSC) module is integrated in the backbone network to slice the feature maps and enhance the features so as to effectively retain the features of small objects. Subsequently, to enhance the information exchange between upper and lower layers, we design a special multi-cross-scale feature pyramid network (M-FPN). The C2f-Hierarchical-Phantom Convolution (C2f-HPC) module in the network effectively reduces information loss by fine-grained multi-scale feature fusion. Ultimately, adaptive spatial feature fusion detection Head (ASFFDHead) introduces an additional P2 detection head to enhance the resolution of feature maps to better locate small objects. Moreover, the ASFF mechanism is employed to optimize the detection process by filtering out information conflicts during multi-scale feature fusion, thereby significantly optimizing small object detection capability. Using YOLOv8n as the baseline, SMA-YOLO is evaluated on the VisDrone2019 dataset, achieving a 7.4% improvement in mAP@0.5 and a 13.3% reduction in model parameters, and we also verified its generalization ability on VAUDT and RSOD datasets, which demonstrates the effectiveness of our approach. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Machine Learning Applications on Remote Sensing Images)
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23 pages, 16886 KiB  
Article
SAVL: Scene-Adaptive UAV Visual Localization Using Sparse Feature Extraction and Incremental Descriptor Mapping
by Ganchao Liu, Zhengxi Li, Qiang Gao and Yuan Yuan
Remote Sens. 2025, 17(14), 2408; https://doi.org/10.3390/rs17142408 - 12 Jul 2025
Viewed by 285
Abstract
In recent years, the use of UAVs has become widespread. Long distance flight of UAVs requires obtaining precise geographic coordinates. Global Navigation Satellite Systems (GNSS) are the most common positioning models, but their signals are susceptible to interference from obstacles and complex electromagnetic [...] Read more.
In recent years, the use of UAVs has become widespread. Long distance flight of UAVs requires obtaining precise geographic coordinates. Global Navigation Satellite Systems (GNSS) are the most common positioning models, but their signals are susceptible to interference from obstacles and complex electromagnetic environments. In this case, vision-based technology can serve as an alternative solution to ensure the self-positioning capability of UAVs. Therefore, a scene adaptive UAV visual localization framework (SAVL) is proposed. In the proposed framework, UAV images are mapped to satellite images with geographic coordinates through pixel-level matching to locate UAVs. Firstly, to tackle the challenge of inaccurate localization resulting from sparse terrain features, this work proposes a novel feature extraction network grounded in a general visual model, leveraging the robust zero-shot generalization capability of the pre-trained model and extracting sparse features from UAV and satellite imagery. Secondly, in order to overcome the problem of weak generalization ability in unknown scenarios, a descriptor incremental mapping module was designed, which reduces multi-source image differences at the semantic level through UAV satellite image descriptor mapping and constructs a confidence-based incremental strategy to dynamically adapt to the scene. Finally, due to the lack of annotated public datasets, a scene-rich UAV dataset (RealUAV) was constructed to study UAV visual localization in real-world environments. In order to evaluate the localization performance of the proposed framework, several related methods were compared and analyzed in detail. The results on the dataset indicate that the proposed method achieves excellent positioning accuracy, with an average error of only 8.71 m. Full article
(This article belongs to the Special Issue Advances in Computer Vision and Machine Learning Applications on Remote Sensing Images)
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