Endmember labeling and Spectral Library building and updating based on hyperspectral images

This paper presents a new methodology for automatically labeling extracted endmembers from hyperspectral images, using a reference Spectral Library (SL). The SL is structured with three main layers: main consisting material, semantic meaning, and illumination conditions in order to classify the endmembers. The labeling process is based on two similarity metrics: the Spectral Angle Mapper and the Cross Correlation. These two metrics are initially introduced in spectral inequalities which characterize each endmember of the reference SL. During the labeling procedure, inequalities should be respected for labeling the endmembers, otherwise the values of metrics are used. Updating of the SL refers to incorporation of the not labeled endmembers in the SL. Photo-interpretation methodology and SAM calculation for checking the shifted values due to illumination variations are required. The proposed methodology is evaluated using CASI-550 radiance images. The methodology produced satisfactory results.