Target detection for very high-frequency synthetic aperture radar ground surveillance PROJECT TITLE :Target detection for very high-frequency synthetic aperture radar ground surveillanceABSTRACT:A target detection algorithm is developed based on a supervised learning technique that maximises the margin between two classes, that is, the target class and the non-target class. Specifically, the proposed target detection algorithm consists of (i) image differencing, (ii) maximum-margin classifier, and (iii) diversity combining. The image differencing is to enhance and highlight the targets so that the targets are more distinguishable from the background. The maximum-margin classifier is based on a recently developed feature weighting technique called Iterative RELIEF; the objective of the maximum-margin classifier is to achieve robustness against uncertainties and clutter. The diversity combining utilises multiple images to further improve the performance of detection, and hence it is a type of multi-pass change detection. The authors evaluate the performance of the proposed detection algorithm, using the CARABAS-II synthetic aperture radar (SAR) image data and the experimental results demonstrate superior performance of the proposed algorithm, compared to the benchmark algorithm. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest One class boundary method classifiers for application in a video-based fall detection system Fast tracking algorithm using modified potential function
