Level Set Hyperspectral Segmentation: Near-Optimal Speed Functions using Best Band Analysis and Scaled Spectral Angle Mapper

This paper presents a semi-automated supervised level set hyperspectral image segmentation algorithm. The proposed method uses near-optimal speed functions (which control the level set segmentation) that are composed of a spectral similarity term and a stopping term. The spectral similarity term is used to compare pixels to class training signatures and is based on an optimized best bands analysis (BBA) procedure developed previously by the authors (2). The stopping term is created from a new BBA algorithm, which uses a modified version of the spectral angle mapper (SAM) called the scaled SAM (SSAM). The algorithm is validated with a HYDICE hyperspectral image of the Washington, D.C. Mall. The results of the proposed method are compared to previous results by the authors and show the efficacy of the new algorithm. The contributions of the paper include a nearly-optimal set of speed functions for hyperspectral level set analysis and an automated BBA algorithm based on the SSAM metric for creating the level set stopping term.