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Computer Vision and Pattern Recognition Based on Remote Sensing
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Computer Vision and Pattern Recognition Based on Remote Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 1068

Special Issue Editors

Prof. Dr. Shuaiqi Liu

E-Mail Website
Guest Editor
1. College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
2. Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China
3. National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: computer vision and multidimensional signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Qi Hu

E-Mail
Guest Editor
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
Interests: image processing; signal processing
Prof. Dr. Yudong Zhang

grade E-Mail Website
Guest Editor
Informatics Building School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: explainable deep learning; medical image analysis; pattern recognition and medical sensors; artificial intelligence; intelligent computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue entitled “Computer Vision and Pattern Recognition Based on Remote Sensing” focuses on the latest advancements in and research on utilizing computer vision and pattern recognition techniques to analyze and interpret remote sensing data. Remote sensing technology, which captures information about the Earth's surface from a distance, has become increasingly important in various fields such as urban planning, environmental monitoring, agricultural management, disaster assessment, and map updating. The integration of computer vision and pattern recognition methods with remote sensing data has significantly enhanced our ability to detect, analyze, and understand changes in the environment at large scales and with high accuracy.

This Special Issue showcases cutting-edge research on the development of novel algorithms, frameworks, and applications that leverage remote sensing data for visual analysis, object recognition, scene comprehension, and change detection. The contributions will cover a wide range of topics, including but not limited to:

  • Image processing for remote sensing;
  • Object recognition and instance segmentation;
  • Defect detection in industrial surfaces;
  • Cross-domain object detection;
  • Multimodal detection for autonomous driving;
  • Advanced network architectures for remote sensing image analysis.

Prof. Dr. Shuaiqi Liu
Dr. Qi Hu
Prof. Dr. Yudong Zhang
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. Sensors 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 2600 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

  • image processing
  • object recognition
  • remote sensing

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Published Papers (3 papers)

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Research

22 pages, 9294 KiB  
Article
Deep Layered Network Based on Rotation Operation and Residual Transform for Building Segmentation from Remote Sensing Images
by Shuzhe Zhang, Taoyi Chen, Fei Su, Hao Xu, Yan Li and Yaohui Liu
Sensors 2025, 25(8), 2608; https://doi.org/10.3390/s25082608 - 20 Apr 2025
Viewed by 126
Abstract
Deep learning has been widely applied in building segmentation from high-resolution remote sensing (HRS) images. However, HRS images suffer from insufficient complementary representation of target points in terms of capturing details and global information. To this end, we propose a novel building segmentation [...] Read more.
Deep learning has been widely applied in building segmentation from high-resolution remote sensing (HRS) images. However, HRS images suffer from insufficient complementary representation of target points in terms of capturing details and global information. To this end, we propose a novel building segmentation model for HRS images, termed C_ASegformer. Specifically, we design a Deep Layered Enhanced Fusion (DLEF) module to integrate hierarchical information from different receptive fields, thereby enhancing the feature representation capability of HRS information from global to detailed levels. Additionally, we introduce a Triplet Attention (TA) Module, which establishes dependency relationships between buildings and the environment through multi-directional rotation operations and residual transformations. Furthermore, we propose a Multi-Level Dilated Connection (MDC) Module to efficiently capture contextual relationships across different scales at a low computational cost. We conduct comparative experiments with several state-of-the-art models on three datasets, including the Massachusetts dataset, the INRIA dataset, and the WHU dataset. On the Massachusetts dataset, C_ASegformer achieves 95.42%, 85.69%, and 75.46% for OA, F1score, and mIoU, respectively. C_ASegformer shows more accurate performance, demonstrating the validity and sophistication of the model. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Remote Sensing)
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23 pages, 11146 KiB  
Article
Stereo Online Self-Calibration Through the Combination of Hybrid Cost Functions with Shared Characteristics Considering Cost Uncertainty
by Wonju Lee
Sensors 2025, 25(8), 2565; https://doi.org/10.3390/s25082565 - 18 Apr 2025
Viewed by 183
Abstract
Stereo cameras and stereo matching algorithms are core components for stereo digital image correlation to obtain 3D data robustly in various environments. However, its accuracy heavily relies on extrinsic calibration. In this work, we propose a markerless method for obtaining stereo extrinsic calibration [...] Read more.
Stereo cameras and stereo matching algorithms are core components for stereo digital image correlation to obtain 3D data robustly in various environments. However, its accuracy heavily relies on extrinsic calibration. In this work, we propose a markerless method for obtaining stereo extrinsic calibration by employing nonlinear optimization on a manifold, which leverages the inherent observability property. To ensure the stability of the optimization and the robustness to outliers when using natural features, we minimize the error constraint between spatial per-frame sparse natural features by stably combining cost functions with similar properties, considering cost uncertainty. Both constraints work in the same direction to reduce the difference in the y-axis coordinates of corresponding points. As a result, the optimization process proceeds smoothly, and it helps reduce the likelihood of overfitting. To extend the problem to the spatiotemporal domain, Bayesian filtering is applied using the logit of zero-shot-based semantic segmentation. Using publicly available data, we conducted experiments where the optimization converged with minimal variation in the number of iterations, and stability was validated through a comparison with state-of-the-art methods. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Remote Sensing)
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18 pages, 17109 KiB  
Article
DCN-YOLO: A Small-Object Detection Paradigm for Remote Sensing Imagery Leveraging Dilated Convolutional Networks
by Meilin Xie, Qiang Tang, Yuan Tian, Xubin Feng, Heng Shi and Wei Hao
Sensors 2025, 25(7), 2241; https://doi.org/10.3390/s25072241 - 2 Apr 2025
Viewed by 307
Abstract
With the rapid development of remote sensing technology, optical remote sensing images are increasingly being used in areas such as military reconnaissance, environmental monitoring, and urban planning. Due to the small number of pixels, fuzzy features, and complex background, it is difficult for [...] Read more.
With the rapid development of remote sensing technology, optical remote sensing images are increasingly being used in areas such as military reconnaissance, environmental monitoring, and urban planning. Due to the small number of pixels, fuzzy features, and complex background, it is difficult for conventional convolutions to effectively extract features from small objects. To address this problem, we propose to use multi-scale dilated convolutions to increase the receptive field size of the model to adapt to changes in object size, capture multi-scale contextual information of the feature map, and extract richer object features. First, we propose a Dilated Convolutional Residual (DCR) module for high-level feature extraction in the network. Second, the context aggregation (CONTEXT) module uses remote interaction to perform associative computation on images using contextual aggregation, allowing the model to understand the global semantic information of the image. We propose a novel object detection method, DCN-YOLO, which achieves an AP50 of 56.6 on the AI-TOD dataset, effectively improving the detection accuracy and robustness of small objects in remote sensing images. It provides a new technical approach to the detection of small objects in remote sensing. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Remote Sensing)
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