HYPERSPECTRAL UNMIXING BASED VEGETATION DETECTION WITH SEGMENTATION

This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spectral library with each pixel of hyperspectral data cube employing Spectral Angle Mapper (SAM) algorithm. The pixel(s) with the best match are assumed to be the most likely target vegetation locations. The regions around these potential target locations are further analyzed via hyperspectral unmixing techniques to obtain the real spectra in the image. The abundance fractions are evaluated so as to compare the algorithm performance with those of other methods. As a post processing technique meanshift segmentation algorithm utilized