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J. Imaging
Special Issues
AI-Driven Remote Sensing Image Processing and Pattern Recognition
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AI-Driven Remote Sensing Image Processing and Pattern Recognition

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computer Vision and Pattern Recognition".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 3713

Editors

Dr. Zhenqi Liu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: deep learning; image processing; remote sensing; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jiaxin Li

E-Mail Website
Guest Editor
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: data fusion; hyperspectral fusion; super resolution; change detection; registration; classification; denoising; unmixing; segmentation; anomaly detection; transformer; diffusion; self-supervised
Special Issues, Collections and Topics in MDPI journals
Dr. Mengying Xie

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400030, China
Interests: image processing; remote sensing; pattern recognition
Dr. Chuan Fu

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400030, China
Interests: hyperspectral image processing; remote sensing; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Kaiwen Wei

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400030, China
Interests: deep learning; multimodal data fusion; explainable AI

Special Issue Information

Dear Colleagues,

The field of remote sensing imaging science is undergoing a transformative era, driven by advancements in artificial intelligence, sensor technology, and computational power. This Special Issue, "AI-Driven Remote Sensing Image Processing and Pattern Recognition," seeks to highlight cutting-edge research and comprehensive reviews that address the evolving landscape of extracting meaningful information from complex remote sensing image data.

This Special Issue aims to focus on novel methodologies that enhance the capabilities of remote sensing image analysis systems. Topics of interest include, but are not limited to, the following: Deep learning architectures (CNNs, transformers, GANs) for remote sensing image segmentation, remote sensing image detection, and remote sensing image classification; explainable AI (XAI) for interpretable model decisions; multi-modal and cross-domain remote sensing data fusion; and robust algorithms for low-quality or adversarial data. A key emphasis will be on practical challenges such as remote sensing data efficiency, model generalization, real-time processing, and integration with emerging imaging modalities.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advanced deep learning architectures for remote sensing (e.g., transformers, generative models);
  • Multimodal and multi-temporal remote sensing data fusion;
  • Explainable AI for remote sensing image analysis;
  • Scalable and efficient processing techniques;
  • Robustness to noise, occlusion, and atmospheric conditions;
  • Real-time processing and automated systems.

We look forward to receiving your contributions.

Dr. Zhenqi Liu
Dr. Jiaxin Li
Dr. Mengying Xie
Dr. Chuan Fu
Dr. Kaiwen Wei
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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 segmentation
  • deep learning
  • object detection
  • pattern recognition
  • computer vision
  • multimodal data fusion
  • explainable AI (XAI)
  • image classification
  • computational imaging

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

Published Papers (3 papers)

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Research

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30 pages, 21776 KB  
Article
LDSNet: A Lightweight Detail-Sensitive Network for Small Object Detection in Low-Altitude UAV Scenarios
by Tong Tan, Xianrong Peng, Jianlin Zhang, Haorui Zuo, Yao Zhang, Yunhao Wu and Hui Li
J. Imaging 2026, 12(5), 209; https://doi.org/10.3390/jimaging12050209 - 14 May 2026
Viewed by 654
Abstract
Object detection in Unmanned Aerial Vehicle (UAV) imagery faces significant challenges due to the unique aerial perspective. A major bottleneck is the weak feature representation of small objects, which limits both detection accuracy and computational efficiency. To address this issue, we propose a [...] Read more.
Object detection in Unmanned Aerial Vehicle (UAV) imagery faces significant challenges due to the unique aerial perspective. A major bottleneck is the weak feature representation of small objects, which limits both detection accuracy and computational efficiency. To address this issue, we propose a Lightweight Detail-Sensitive Network (LDSNet). Specifically, LDSNet consists of three key components: (1) Lightweight Detail-Sensitive Downsampling (LDSDown), which combines anti-aliasing smoothing with dual-path feature extraction to preserve the spatial details of small objects during downsampling; (2) Shared Recursive Dilated Convolution (SRDC), which uses weight-shared multi-rate dilated convolutions to capture multi-scale context and enlarge the receptive field without introducing extra parameters; and (3) Deeply Decoupled Grouped Head (DGHead), which employs high-ratio grouped convolutions to significantly reduce the computational cost of processing high-resolution inputs. Extensive experiments on the VisDrone2019 and HIT-UAV datasets demonstrate that LDSNet achieves an excellent trade-off between accuracy and efficiency. Compared to the YOLOv11n baseline, LDSNet reduces parameters by 84.6% (from 2.6 M to 0.4 M) and FLOPs by 29.2% (from 6.5 G to 4.6 G), while improving mAP50 by 2.2% on VisDrone2019 and achieving 94.5% on HIT-UAV. Full article
(This article belongs to the Special Issue AI-Driven Remote Sensing Image Processing and Pattern Recognition)
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18 pages, 1564 KB  
Article
Salient Object Detection in Optical Remote Sensing Images Based on Hierarchical Semantic Interaction
by Jingfan Xu, Qi Zhang, Jinwen Xing, Mingquan Zhou and Guohua Geng
J. Imaging 2025, 11(12), 453; https://doi.org/10.3390/jimaging11120453 - 17 Dec 2025
Viewed by 905
Abstract
Existing salient object detection methods for optical remote sensing images still face certain limitations due to complex background variations, significant scale discrepancies among targets, severe background interference, and diverse topological structures. On the one hand, the feature transmission process often neglects the constraints [...] Read more.
Existing salient object detection methods for optical remote sensing images still face certain limitations due to complex background variations, significant scale discrepancies among targets, severe background interference, and diverse topological structures. On the one hand, the feature transmission process often neglects the constraints and complementary effects of high-level features on low-level features, leading to insufficient feature interaction and weakened model representation. On the other hand, decoder architectures generally rely on simple cascaded structures, which fail to adequately exploit and utilize contextual information. To address these challenges, this study proposes a Hierarchical Semantic Interaction Module to enhance salient object detection performance in optical remote sensing scenarios. The module introduces foreground content modeling and a hierarchical semantic interaction mechanism within a multi-scale feature space, reinforcing the synergy and complementarity among features at different levels. This effectively highlights multi-scale and multi-type salient regions in complex backgrounds. Extensive experiments on multiple optical remote sensing datasets demonstrate the effectiveness of the proposed method. Specifically, on the EORSSD dataset, our full model integrating both CA and PA modules improves the max F-measure from 0.8826 to 0.9100 (↑2.74%), increases maxE from 0.9603 to 0.9727 (↑1.24%), and enhances the S-measure from 0.9026 to 0.9295 (↑2.69%) compared with the baseline. These results clearly demonstrate the effectiveness of the proposed modules and verify the robustness and strong generalization capability of our method in complex remote sensing scenarios. Full article
(This article belongs to the Special Issue AI-Driven Remote Sensing Image Processing and Pattern Recognition)
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Review

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35 pages, 4998 KB  
Review
A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images
by Haoze Zheng, Heran Wang, Hualong Dong and Yurong Qian
J. Imaging 2026, 12(2), 66; https://doi.org/10.3390/jimaging12020066 - 5 Feb 2026
Cited by 2 | Viewed by 1522
Abstract
Major crops worldwide are affected by various diseases yearly, leading to crop losses in different regions. The primary methods for addressing crop disease losses include manual inspection and chemical control. However, traditional manual inspection methods are time-consuming, labor-intensive, and require specialized knowledge. The [...] Read more.
Major crops worldwide are affected by various diseases yearly, leading to crop losses in different regions. The primary methods for addressing crop disease losses include manual inspection and chemical control. However, traditional manual inspection methods are time-consuming, labor-intensive, and require specialized knowledge. The preemptive use of chemicals also poses a risk of soil pollution, which may cause irreversible damage. With the advancement of computer hardware, photographic technology, and artificial intelligence, crop disease recognition methods based on spectral and red–green–blue (RGB) images not only recognize diseases without damaging the crops but also offer high accuracy and speed of recognition, essentially solving the problems associated with manual inspection and chemical control. This paper summarizes the research on disease recognition methods based on spectral and RGB images, with the literature spanning from 2020 through early 2025. Unlike previous surveys, this paper reviews recent advances involving emerging paradigms such as State Space Models (e.g., Mamba) and Generative AI in the context of crop disease recognition. In addition, it introduces public datasets and commonly used evaluation metrics for crop disease identification. Finally, the paper discusses potential issues and solutions encountered during research, including the use of diffusion models for data augmentation. Hopefully, this survey will help readers understand the current methods and effectiveness of crop disease detection, inspiring the development of more effective methods to assist farmers in identifying crop diseases. Full article
(This article belongs to the Special Issue AI-Driven Remote Sensing Image Processing and Pattern Recognition)
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