Target Recognition in Synthetic Aperture Radar Imagery
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 18720
Special Issue Editors
Interests: statistical signal processing applied to radar target recognition global navigation satellite system reflectometry, and hyperspectral unmixing; elaboration of satellite data for Earth observation with application in imaging and sounding with passive (multispectral and hyperspectral) and active (SAR, GNSS-R) sensors
Special Issues, Collections and Topics in MDPI journals
Interests: statistical signal processing with emphasis on radar/SAR signal processing; radar targets classification; polarimetric radar/SAR
Special Issues, Collections and Topics in MDPI journals
Interests: automatic target recognition; passive/forward scattering radars; motions modelling and micro-doppler analysis; joint radar communication operations; MIMO Radar; cognitive radars and AI
Special Issue Information
Dear Colleagues,
In recent years the interest towards the development of algorithms aimed at automatically classifying targets in Synthetic Aperture Radar (SAR) images is growing more and more. Particularly, the knowledge of the types of man-made objects (like missile launchers, vehicles, planes) that are positioned in the observed scene could be a task of paramount importance in the modern surveillance systems to understand possible threats in military contexts, but also to properly manage some activities in a specific area in civil environments.
The scope of this Special Issue is to provide an overview of signal processing methods for target recognition. Contributions to the body of knowledge in the field could be from polarimetric synthetic aperture radar (SAR), inverse SAR (ISAR) and passive bistatic radar, with applications of interest in automatic target recognition (ATR) and its lower level tasks (identification, characterization and fingerprinting).
The application of Artificial Intelligence (AI) techniques to ATR are also very welcomed, as it recently proved to represent an interesting and useful alternate processing strategy. The efforts in this field should highlight the capabilities and limitations of AI for effective application to ATR problems.
Dr. Addabbo Pia
Dr. Luca Pallotta
Dr. Christos Ilioudis
Guest Editors
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Keywords
- Synthetic Aperture Radar
- Automatic Target Recognition
- Classification
- Features Extraction
- Artificial Intelligence
- Machine Learning
- Neural Networks
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