An adaptive hierarchical segmentation algorithm based on quadtree decomposition for hyperspectral imagery
We present an adaptive hierarchical segmentation algorithm based on quadtree decomposition and a modified minimum-distance classifier. The proposed algorithm uses quadtree decomposition because this technique can adapt to the local characteristics of the hyperspectral data. A feature vector (i.e., a class centroid) for each material type is recursively estimated and updated, so that increasingly accurate segmentation results are achieved as the decomposition proceeds. The proposed method provides improved segmentation performance over standard template-matching segmentation techniques because it adapts to the local context. It also imposes a spatial smoothness constraint on the pixel classification that provides spatial continuity during the segmentation process. Both the proposed algorithm and a standard template-matching technique were applied to a set of visible to near-infrared hyperspectral images results are presented.