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Sensors
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Artificial Intelligence-Based Target Recognition and Remote Sensing Data Processing
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Artificial Intelligence-Based Target Recognition and Remote Sensing Data Processing

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

Deadline for manuscript submissions: 20 March 2025 | Viewed by 2190

Special Issue Editors

Dr. Abdelmalek Toumi

E-Mail Website
Guest Editor
Lab-STICC UMR CNRS 6285, ENSTA, 29806 Bretagne, France
Interests: target recognition; radar; classification; deep learning; computer science; remote sensing; data fusion.
Prof. Dr. Ali Khenchaf

E-Mail Website
Guest Editor
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: computer science; engineering; observation; propagation; wave scattering; scattering in random media; monostatic and bistatic scattering; electromagnetic radar cross section; sea clutter; active and passive sensors (Radar, Lidar, Optics, GNSS); radar applications; data assimilation (n-D); sea surface and environment; extraction of parameters from the observed scene: imagery and target parameter estimation; direct and inverse problems; remote sensing of the ocean and the environment
Special Issues, Collections and Topics in MDPI journals
Dr. Jean-Christophe Cexus

E-Mail Website
Guest Editor
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: signal and image processing; machine learning; computer science; engineering; remote sensing; radar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the remote sensing community has gathered an unprecedented amount of data, which is boosting the development of an increasing number of artificial intelligence (AI)-based applications for monitoring environments and data analysis in military and civilian domains. Leveraging this wealth of data, researchers have increasingly turned to advanced AI techniques to enhance target recognition and process remote sensing data effectively.

This Special Issue aims to gather cutting-edge research and innovative methodologies in the field of target recognition, with a particular emphasis on the combination of AI and remote sensing applications. This issue naturally covers a broad range of topics on theoretical, application-oriented, and experimental studies in remote sensing and AI-based target recognition, including, but not limited to, the following:

  • Deep learning algorithms for target detection and classification
  • Detection and tracking of objects by remote sensing
  • AI operated in remote sensing using data from different sensors (e.g. camera, lidar, radar and sonar)
  • Feature extraction and representation learning from remote sensing data
  • Fusion of multi-modal and multi-temporal remote sensing data
  • Learning transfer and domain adaptation in remote sensing applications
  • Novel applications of AI in remote sensing for environmental monitoring, climate, disaster management, urban planning, agriculture, etc.

Dr. Abdelmalek Toumi
Prof. Dr. Ali Khenchaf
Dr. Jean-Christophe Cexus
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

  • target recognition
  • machine learning
  • deep learning
  • remote sensing
  • environmental monitoring
  • data fusion

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Published Papers (1 paper)

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Research

23 pages, 43902 KiB  
Article
OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion
by Yangcheng Bu, Hairong Ye, Zhixin Tie, Yanbing Chen and Dingming Zhang
Sensors 2024, 24(11), 3596; https://doi.org/10.3390/s24113596 - 3 Jun 2024
Cited by 2 | Viewed by 1791
Abstract
As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes [...] Read more.
As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes and small, densely packed targets, there are many challenges in detecting remote sensing objects. Those challenges lead to insufficient accuracy in remote sensing object detection. Consequently, developing a new model is essential to enhance the identification capabilities for objects in remote sensing imagery. To solve these constraints, we have designed the OD-YOLO approach that uses multi-scale feature fusion to improve the performance of the YOLOv8n model in small target detection. Firstly, traditional convolutions have poor recognition capabilities for certain geometric shapes. Therefore, in this paper, we introduce the Detection Refinement Module (DRmodule) into the backbone architecture. This module utilizes Deformable Convolutional Networks and the Hybrid Attention Transformer to strengthen the model’s capability for feature extraction from geometric shapes and blurred objects effectively. Meanwhile, based on the Feature Pyramid Network of YOLO, at the head of the model framework, this paper enhances the detection capability by introducing a Dynamic Head to strengthen the fusion of different scales features in the feature pyramid. Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. Experiments on the VisDrone dataset show that OD-YOLO surpasses the compared models by at least 5.2% in mAP50 and 4.4% in mAP75, and experiments on the Foggy Cityscapes dataset demonstrated that OD-YOLO improved mAP by 6.5%, demonstrating outstanding results in tasks related to remote sensing images and adverse weather object detection. This work not only advances the research in remote sensing image analysis, but also provides effective technical support for the practical deployment of future remote sensing applications. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Target Recognition and Remote Sensing Data Processing)
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