Development Of Spectral Indexes In Hyperspectral Imagery For Land Cover Assessment

Spectral indexes (SI) are widely used for land cover characterization and also in several physical models for the study of land surface processes. For example, the normalized differenced vegetation index (NDVI) is used in the characterization of soil moisture along with shortwave infrared reflectance. However, for hyperspectral imagery (HSI) comprising many bands within a single spectrum, it is significant to identify the optimal bands for the development of SI. In this paper, we study the potential of band selection in specific bandwidths for the determination of SI. The proposed methodology includes two strategies for development of SI: direct SI determined by the best band within specific spectrums and fused SI determined by fusion of two best bands within specific spectrums. The experiments are conducted using three datasets, two corresponding to snow-covered areas, studied using the normalized differenced snow index (NDSI) and one comprising the agricultural area, studied using NDVI. The developed SI are evaluated through a comparison with the supervised classification maps from the corresponding HSI. A kappa coefficient of 0.693, 0.726 and 0.803 was observed between the results obtained from histogram slicing of SI with respect to the classification maps for the three datasets, respectively.