An adaptive segmentation algorithm using iterative local feature extraction for hyperspectral imagery
We present an adaptive segmentation algorithm based on the iterative use of a modified minimum-distance classifier. Local adaptivity is achieved by gradually updating each class centroid over a local region whose size is reduced progressively during a segmentation process. The proposed method provides improved segmentation performance over template matching segmentation techniques because it adapts to the local context. The proposed algorithm can be applied to virtually any hyperspectral image regardless of size, dimensionality, and spectral sensitivity. Experimental results on a set of visible to near-infrared hyperspectral images using both the proposed algorithm and a standard template matching technique are presented.