Hierarchical Band Selection Using the N-Dimensional Solid Spectral Angle Method to Address Inter- and Intra- Class Spectral Variability

The contiguous narrow bands in hyperspectral data can hamper the accurate discrimination of targets especially for spectrally similar materials. The N-dimensional Solid Spectral Angle (NSSA) is a novel method that selects important bands for the maximum spectral separation of materials. This paper proposed a strategy of hierarchical band selection based on the NSSA method to address inter-and intra-class variability among materials. Bands are separately selected from different hierarchies of categorized materials using NSSA and the individual band sets are then combined. To evaluate this Hierarchical Band Selection with NSSA (HBS-NSSA), two hyperspectral datasets were analyzed that include airborne image endmembers for geological mapping and leaf spectra for tree species discrimination. Selected bands agree well with known features identified from expert knowledge. The results suggest that the HBS-NSSA method is both practical and effective and could be easily adopted in any other fields of application with spectrally similar materials.