Joint Artificial Intelligence and Computer Vision Applications in Remote Sensing

Dear Colleagues,

Thanks to the proliferation of satellites and aerial vehicles, including Unmanned Aerial Vehicles (UAVs), equipped with several sensors, including Laser, Lidar, and multi-spectral and hyperspectral cameras, and thanks to improvements in data storage and transfer capabilities, today Remote Sensing produces large quantities of data in the spectral, temporal, and spatial domains. The imagery analytics and interpretation cannot be performed by human imagery analysts any more. Techniques for automating the image analysis process would be advanced by the inclusion of Artificial Intelligence and Computer Vision techniques in the design of most remote sensing applications. Classical machine learning techniques, from neural networks to fuzzy rules and Support Vector Machine algorithms, as well as novel Deep Learning approaches, have been proposed to address the more challenging remote sensing problems. This Special Issue focuses on current research on artificial intelligence and computer vision for remote sensing applications. Just some examples of the topics addressed are the automated analysis of multispectral imagery, normalization (weather, lenses, and natural changes in lighting throughout the day/year) of satellite images, land-use land-cover classification, precision agriculture, change detection, semantic segmentation, object/target detection and recognition, 3D reconstruction and shape modeling, intelligent onboard processing, advanced database interrogation, data fusion technologies, urban planning, environmental quality monitoring, humanitarian crisis management, etc.

Prof. Primo Zingaretti
Prof. Adriano Mancini
Dr. Chen Chen
Guest Editors

Manuscript Submission Information

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