Normalized Spectral Similarity Score ( ${\hbox {NS}}^{3}$ ) as an Efficient Spectral Library Searching Method for Hyperspectral Image Classification

We present a new spectral library search algorithm, referred to as normalized spectral similarity score (NS³), for improved accuracy in airborne hyperspectral image classification. The proposed library search algorithm combines the relative merits of spectral angle and amplitude differences inherent in a hyperspectral image and reference library reflectance spectra. Various spectral libraries constructed from the field reflectance spectra collected during two successive growing seasons were used for classification of a historical HyMAP hyperspectral image for crop classification by spectral library search approach. The performance of the proposed method was compared with existed spectral library search methods, i.e., spectral angle mapper (SAM), spectral correlation mapper (SCM), spectral information divergence (SID), and the classical maximum likelihood classifier (MLC). The best classification accuracy obtained from the proposed NS³ library search method (74.71%) was significantly lower than that of the MLC supervised classification (85.44%). However, a comparative analysis of the classification accuracy indicates the enhanced performance of the proposed NS³ method for transferring a spectral library for HyMAP image classification, because the classification accuracies of the other library search methods tested were considerably lower (MC (61,87%), SAM (54,10%), SCM (52,51%), and SID (34,30 %)). Furthermore, various factors that influence the performance of spectral library search method for hyperspectral image classification are discussed.