Artificial Intelligence-Based Target Recognition and Remote Sensing Data Processing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 72
Special Issue Editors
Interests: target recognition; radar; classification; deep learning; computer science; remote sensing; data fusion.
Interests: computer science; engineering; observation; propagation; wave scattering; scattering in random media; monostatic and bistatic scattering; electromagnetic radar cross section; sea clutter; active and passive sensors (Radar, Lidar, Optics, GNSS); radar applications; data assimilation (n-D); sea surface and environment; extraction of parameters from the observed scene: imagery and target parameter estimation; direct and inverse problems; remote sensing of the ocean and the environment
Special Issues, Collections and Topics in MDPI journals
Interests: signal and image processing; machine learning; computer science; engineering; remote sensing; radar
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent decades, the remote sensing community has gathered an unprecedented amount of data, which is boosting the development of an increasing number of artificial intelligence (AI)-based applications for monitoring environments and data analysis in military and civilian domains. Leveraging this wealth of data, researchers have increasingly turned to advanced AI techniques to enhance target recognition and process remote sensing data effectively.
This Special Issue aims to gather cutting-edge research and innovative methodologies in the field of target recognition, with a particular emphasis on the combination of AI and remote sensing applications. This issue naturally covers a broad range of topics on theoretical, application-oriented, and experimental studies in remote sensing and AI-based target recognition, including, but not limited to, the following:
- Deep learning algorithms for target detection and classification
- Detection and tracking of objects by remote sensing
- AI operated in remote sensing using data from different sensors (e.g. camera, lidar, radar and sonar)
- Feature extraction and representation learning from remote sensing data
- Fusion of multi-modal and multi-temporal remote sensing data
- Learning transfer and domain adaptation in remote sensing applications
- Novel applications of AI in remote sensing for environmental monitoring, climate, disaster management, urban planning, agriculture, etc.
Dr. Abdelmalek Toumi
Prof. Dr. Ali Khenchaf
Dr. Jean-Christophe Cexus
Guest Editors
Manuscript Submission Information
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Keywords
- target recognition
- machine learning
- deep learning
- remote sensing
- environmental monitoring
- data fusion
