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Advances in Remote Sensing Video Data Processing: Theories, Technologies and Applications

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

Deadline for manuscript submissions: 30 December 2024 | Viewed by 1082

Special Issue Editors


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Guest Editor
School of Microelectronics (School of Integrated Circuits), Nanjing University of Science and Technology (NJUST), Nanjing 210000, China
Interests: microwave remote sensing; synthetic aperture radar; video radar system; phased array radar; signal processing; imaging; electronic measurement; electronic countermeasure; computer vision; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: intelligent sensors; machine learning; data analytics; information fusion; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few years, remote sensing video data have gained a lot of attention due to their ability to provide information about the dynamics of the Earth, opening the door for applications that go beyond the limits of static imaging. Video data can be collected using special sensors, e.g., visible light, multispectral, hyperspectral, infrared, lidar, radar, gas or radioactivity sensors, etc. Nevertheless, compared to images, dealing with remote sensing video is inevitably more challenging as it adds the temporal dimension that requires the development of more sophisticated solutions. Therefore, it is urgent for the academic and industrial communities to share the latest research findings on the theories, technologies and applications of remote sensing video data.

This Special Issue encourages scholars to publish research papers and review articles on the remote sensing mechanisms, sensors, detection, recognition, and interpretation technologies of video data, as well as their applications. Discussions on the challenges and limitations of remote sensing video data processing and how they can be addressed are also welcomed.

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

  • Novel remote sensing video missions, systems, and techniques;
  • Video target segmentation, detection, and tracking;
  • Intelligence processing in remote sensing video applications;
  • Video recognition and interpretation approaches for remote sensing;
  • Video data fusion, including integration with satellite, aerial, or terrestrial data and the integration of heterogeneous data;
  • Deformation monitoring, time series data mining, and numerical inversion;
  • Online and real-time processing of remote sensing video data;
  • Other related topics.

Application scenarios include, but are not limited, to resource censuses, environmental and disaster warnings, transportation and urban planning, surveying and mapping, building monitoring, agricultural production, etc.

Other related topics.

Dr. Peng Wang
Dr. Ying Zhang
Prof. Dr. Henry Leung
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 video
  • video system development
  • video detection and tracking
  • image/video understanding
  • video data processing
  • machine learning

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Published Papers (1 paper)

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Research

22 pages, 4305 KiB  
Article
LiOSR-SAR: Lightweight Open-Set Recognizer for SAR Imageries
by Jie Yang, Jihong Gu, Jingyu Xin, Zhou Cong and Dazhi Ding
Remote Sens. 2024, 16(19), 3741; https://doi.org/10.3390/rs16193741 - 9 Oct 2024
Viewed by 718
Abstract
Open-set recognition (OSR) from synthetic aperture radar (SAR) imageries plays a crucial role in maritime and terrestrial monitoring. Nevertheless, numerous deep learning-based SAR classifiers struggle with unknown targets outside of the training dataset, leading to a dilemma, namely that a large model is [...] Read more.
Open-set recognition (OSR) from synthetic aperture radar (SAR) imageries plays a crucial role in maritime and terrestrial monitoring. Nevertheless, numerous deep learning-based SAR classifiers struggle with unknown targets outside of the training dataset, leading to a dilemma, namely that a large model is difficult to deploy, while a smaller one sacrifices accuracy. To address this challenge, the novel “LiOSR-SAR” lightweight recognizer is proposed for OSR in SAR imageries. It incorporates the compact attribute focusing and open-prediction modules, which collectively optimize its lightweight structure and high accuracy. To validate LiOSR-SAR, “fast image simulation using bidirectional shooting and bouncing ray (FIS-BSBR)” is exploited to construct the corresponding dataset. It enhances the details of targets for more accurate recognition significantly. Extensive experiments show that LiOSR-SAR achieves remarkable recognition accuracies of 97.9% and 94.1% while maintaining a compact model size of 7.5 MB, demonstrating its practicality and efficiency. Full article
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