An intensity-gradient-texture guided methodology for spatial segmentation of remotely sensed multi/hyperspectral imagery

We propose a novel unsupervised segmentation method that efficiently exploits spectral intensity, gradient and textural information in remotely sensed imagery. Our approach begins in a multi-band gradient detection scheme of the input scene, utilized to determine the spectral intensity variations across it. The obtained gradient map is employed in an iterative region growth procedure that originates in pixel locations of gradual/low intensity variations and concludes in sites of abrupt intensity transitions, to yield an initial region map. This map is combined with co-occurrence matrices based textural descriptors using a multivariate region merging procedure that fuses regions with similar characteristics to yield the final segmentation. Our approach was tested on several multi/hyperspectral data sets with favorable results.