Evaluation of similarity measure methods for hyperspectral remote sensing data

Taking the standard vegetation spectral library data and the hyperspectral Hyperion remote sensing image, five similarity measure methods (i.e., Euclidean distance, spectral information divergence, spectral angle cosine, spectral correlation coefficient and spectral angle cosine-Euclidean distance) are comprehensively evaluated under a unified testing framework. The results indicate that the spectral angle cosine-Euclidean distance method demonstrates the most superior ability to distinguish various land cover types among five methods because it fully utilizes both the spectral amplitude and shape feature in the hyperspectral data. A combination of the spectral amplitude-sensitive method and the shape-sensitive method will effectively improve the identification accuracy of different land cover types. These evaluation results can be used to guide the selection of an optimal similarity measure method for automatic classification with hyperspectral data.