Microwave Remote Sensing for Object Detection
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 26751
Special Issue Editors
Interests: synthetic apeture radar (SAR) imaging; real-time radar imaging processor; SAR image processing
Special Issues, Collections and Topics in MDPI journals
Interests: Synthetic Aperture Radar (SAR) imaging; none-line-of-sight radar imaging
Interests: Synthetic Apeture Radar (SAR) imaging; moving target imaging; feature enhancement
Special Issue Information
Dear Colleagues,
As a method of microwave remote sensing, synthetic aperture radar (SAR) technology has developed rapidly in recent years, while the SAR image processing is developing towards achieving higher resolution, multi polarization and high processing speeds. By focusing on various imaging scenes such as airports, harbors, complicated land regions or sea, the SAR images can cover different objects such as airplanes, ships, vehicles, etc. The question of how to locate and find interesting targets quickly and accurately using these large-scale SAR images is clearly gaining significance. For instance, real-time ship detection methods in SAR images are conducive to marine resource management, search and rescue and so on. In particular, the detection and recognition method based on deep learning promotes the ability of target detection in microwave images.
This Special Issue aims to include studies that cover different object detection methods based on different microwave remote sensors and platforms. Topics may cover anything from the target detection, target recognition under complicated land regions or sea conditions, to more comprehensive targets and scenes. Hence, both conventional detection methods and new deep learning-based object detection methods, such as convolutional neural networks and transformer networks for microwave images, are welcome.
- Target detection and recognition in microwave images/SAR images;
- Deep learning methods for SAR image understanding;
- Transfer learning and few sample learning in SAR images.
Prof. Dr. Guangcai Sun
Dr. Jiang Qian
Dr. Lei Yang
Dr. Jinsong Zhang
Guest Editors
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Keywords
- Synthetic Aperture Radar (SAR)
- airborne and satellite systems
- objection detection and recognition
- machine learning, compressive sensing
- deep neural network sand few sample learning
- ground moving target indication (GMTI)
- change detection in SAR images
- generative adversarial networks (GANs)
- ship detection and ship traffic
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