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Computer Vision and Remote Sensing – Terrain Feature Classifications


Project Description

The research is mainly concerned with automated image understanding and processing of remotely
sensed data, such as multispectral/hyperspectral images, as well as LIDAR data, which provide height information of terrain. Data sources range from UAV captured data, airborne data, to satellite data. Methodology and algorithms will be developed to classify terrain features in these data, based on advanced techniques of computer vision, image processing, statistical pattern
classification, and machine learning. The application of the research covers land-cover change detection, crop growth monitoring, and may extend to environmental monitoring.

Funding Notes

There is no funding associated with the PhD study. However applicants are encouraged to apply for funding from any funding bodies.


First degree in computer science, physics, engineering, and mathematics with 2:1 or abov. MSc degree in the relevant subject areas is desired.

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