Radar Remote Sensing and Applications—2nd Edition

Dear Colleagues,

Radar sensors are pervasive in remote sensing and related applications. Today, the available radar systems and the corresponding algorithms are still not perfect, but compared to twenty years ago, they are more robust, sophisticated, and user-friendly, and the radar data are of higher quality. Many radar platforms and algorithms are still for specialized tasks/applications only. Therefore, original and innovative methodological contributions to address the key challenge of radar system design and algorithmic data processing are required. As for radar remote sensing data, a key challenge is that systematic acquisitions are still insufficient, despite the availability of a few consolidated products. This hampers applications. Positive attempts to obtain application-oriented radar data have been carried out by many academies, research institutes, and space agencies, but the related research is still quite demanding. Otherwise, deep learning techniques bring the biggest breakthroughs in the machine learning field. In radar remote sensing, deep learning leverages the nature of huge remote sensing data and provides an end-to-end fashion where powerful adaptive feature representations can be automatically learned from the raw data. In response, the purpose of this Special Issue is to provide a platform for the discussion of the major challenges, latest developments, and recent advances in radar remote sensing and SAR-related fields.

Potential topics include, but are not limited to, the following points:

• Advanced radar sensing systems, algorithms, and technologies;
• Synthetic aperture radar (SAR) technologies and applications;
• Millimetre wave radar, Terahertz radar, and LIDAR techniques;
• Distributed radar, MIMO radar imaging, and applications;
• Advanced data acquisition and signal processing for radar applications;
• Novel designing of radar waveform, antenna, and systems;
• Simultaneous localization and mapping (SLAM) with radar sensors;
• Jamming and anti-jamming techniques for radar systems and applications;
• Image processing and multi-sensor fusion in remote sensing scenarios;
• Image interpretation and segmentation, object detection and recognition in radar remote sensing;
• Artificial intelligence and machine learning-based approaches to radar sensing;
• Sensing applications with radar in earth observation, ocean, agriculture, city, and so on;
• Advanced data visualization techniques of radar sensing;
• Object reconstruction from multidimensional radar point clouds.

Dr. Shunjun Wei
Guest Editor

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• radar sensing applications
• remote sensing
• radar imaging and detection
• advanced radar systems
• SLAM
• jamming and anti-jamming
• multi-sensor fusion
• artificial intelligence

• Radar Remote Sensing and Applications in Sensors (10 articles)