Spectral characterization and feature selection is the key to spectral similarity measure which is the basis of both quantitative analysis and accurate object identification for hyperspectral remote sensing. However, spectral similarity measures using only one spectral feature are usually ambiguous in their distinction of similarity. Multiple spectral features integration is needed for objective spectral discrimination. We present a new spectral similarity measure, Spectral Pan-similarity Measure (SPM), based on geometric distance, correlation coefficient and relative entropy. Spectral Pan-similarity Measure objectively quantifies differences between spectra in three spectral features, the vector magnitude, spectral curve shape and spectral information content. The performance of different spectral similarity measures is compared using USGS Mineral Spectral Library and real (i.e., Operational Modular Imaging Spectrometer, OMIS) hyperspectral image. The experimental results demonstrate that the new spectral similarity measure is more effective than the spectral similarity measure taking into account only one or two features both in spectral discriminatory power and spectral identification uncertainty.