Performance evaluation of hyperspectral classification algorithms on AVIRIS mineral data

This study uses three statistics namely Spectral Discriminatory Probability (SDPY), Spectral Discriminatory Entropy (SDE) and Spectral Discriminatory Power (SDPR) to assess the performance of various similarity measures. Similarity measures chosen are Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Jeffries–Matusita distance (JM) and their hybrid combinations of SAM-SID, SCA-SID, and JM-SAM. All the similarity measures and statistics were developed on MATLAB® platform and evaluated the same using freely available AVIRIS mineral data from U.S. Geological Survey spectral library. Analysis of statistical results collectively revealed that among the chosen algorithms SID-SAM and SID-SCA outperform the other similarity measures when tested on mineral data. This result has an important implication on choosing of appropriate similarity measure for mineral classification.
Source: Perspectives in Science - Category: Science Source Type: research