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Remote Sensing
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Advances in Multiple Sensor Fusion and Classification for Object Detection...
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Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 980

Special Issue Editors

Prof. Dr. Chenwei Deng

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Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100811, China
Interests: object detection; object recognition; object tracking
Dr. Wenzheng Wang

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Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: hyperspectral image processing; multi-model fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Jeongho Cho

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Guest Editor
Department of Electrical Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
Interests: object detection and tracking; signal clustering; integrity monitoring

Special Issue Information

Dear Colleagues,

Autonomous object detection, recognition and tracking, as important research topics in remote sensing field, have excellent potential in aerial reconnaissance, environmental perception and management, disaster monitoring, aerial photography and other related applications. Currently, most of the existing algorithms employ a single sensor (e.g., visible light, radar, infrared, etc.) to capture the original images. However, a single sensor can only represent the target from certain specific dimensions. For instance, infrared images are imaged according to the object’s thermal radiation without external light sources, whereas depth sensors can provide 3D position information for the objects. Radar can provide microwave reflection characteristics for the object, despite adverse climatic conditions. Currently, with the rapid development of hardware systems and intelligent platforms, various sensors are integrated into satellite and UAV platforms, allowing for the utilization of their complementary and redundant characteristics. Therefore, in order to achieve and accurate and robust perception system, this Special Issue will focus on advances in multiple sensor fusion and classification for object detection and tracking.

Potential topics for this Special Issue include, but are not limited to:

  • New detection and tracking models with multiple sources/multi-modal information in remote sensing;
  • Efficient target feature representation with multiple modalities;
  • Multi-modal remote sensing data fusion, analysis and understanding;
  • Large-scale multiple sensors/multiple modalities data compressing and transmission;
  • New benchmark datasets including multi-source, multi-modal or multi-dimensional information fusion for object detection, tracking and recognition in remote sensing tasks；
  • Emerging remote sensing applications with multi-source, or multi-dimensional information fusion;
  • Land cover classification and change detection methods based on multi-source data fusion in remote sensing;
  • Domain adaptive data analysis and understanding in remote sensing

Prof. Dr. Chenwei Deng
Dr. Wenzheng Wang
Dr. Jeongho Cho
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

  • multi-modal, multi-dimensional, multi-source information fusion
  • object detection, recognition and tracking
  • earth observation applications
  • feature representation and modeling
  • machine learning and deep learning

Published Papers (1 paper)

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Research

27 pages, 4998 KiB  
Article
A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier
by Xin Chen, Hao Zhang, Shenghao Zhang, Jiapeng Feng, Hui Xia, Peng Rao and Jianliang Ai
Remote Sens. 2024, 16(3), 510; https://doi.org/10.3390/rs16030510 - 29 Jan 2024
Viewed by 592
Abstract
Space infrared dim target recognition is an important applications of space situational awareness (SSA). Due to the weak observability and lack of geometric texture of the target, it may be unreliable to rely only on grayscale features for recognition. In this paper, an [...] Read more.
Space infrared dim target recognition is an important applications of space situational awareness (SSA). Due to the weak observability and lack of geometric texture of the target, it may be unreliable to rely only on grayscale features for recognition. In this paper, an intelligent information decision-level fusion method for target recognition which takes full advantage of the ensemble classifier and Dempster–Shafer (DS) theory is proposed. To deal with the problem that DS produces counterintuitive results when evidence conflicts, a contraction–expansion function is introduced to modify the body of evidence to mitigate conflicts between pieces of evidence. In this method, preprocessing and feature extraction are first performed on the multi-frame dual-band infrared images to obtain the features of the target, which include long-wave radiant intensity, medium–long-wave radiant intensity, temperature, emissivity–area product, micromotion period, and velocity. Then, the radiation intensities are fed to the random convolutional kernel transform (ROCKET) architecture for recognition. For the micromotion period feature, a support vector machine (SVM) classifier is used, and the remaining categories of the features are input into the long short-term memory network (LSTM) for recognition, respectively. The posterior probabilities corresponding to each category, which are the result outputs of each classifier, are constructed using the basic probability assignment (BPA) function of the DS. Finally, the discrimination of the space target category is implemented according to improved DS fusion rules and decision rules. Continuous multi-frame infrared images of six flight scenes are used to evaluate the effectiveness of the proposed method. The experimental results indicate that the recognition accuracy of the proposed method in this paper can reach 93% under the strong noise level (signal-to-noise ratio is 5). Its performance outperforms single-feature recognition and other benchmark algorithms based on DS theory, which demonstrates that the proposed method can effectively enhance the recognition accuracy of space infrared dim targets. Full article
(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)
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