Material Segmentation in Hyperspectral Images with a Spatio-spectral Texture Descriptor

Yu Zhang, Cong Phuoc Huynh, Nariman Habili, King Ngi Ngan

Abstract: In this paper, we address the problem of ground-based hyperspectral image segmentation by combining pixel-level and region-level classification with a region boundary refinement approach. To this end, we represent the spatio-spectral feature of image regions by a descriptor based on Vector of Locally Aggregated Descriptors (VLAD). Further, the region boundaries are refined by minimizing the total region perimeter. Experimental results on a ground-based hyperspectral image dataset clearly demonstrate the advantage of the proposed method over recent prior works, based on several metrics.

External IDs:dblp:conf/icassp/ZhangHHN19