Spectral Curve Shape Matching Using Derivatives in Hyperspectral Images

Abstract

Owning to the absorption of radiation by ground materials on specific wavelengths, spectral curve shapes stay relatively stable in hyperspectral images under various conditions and can be used for material identification. Given a reference spectrum, ground materials can be identified by matching the spectral curve shape of the reference spectrum to that of observed spectra. In the past decade, the spectral curve shape matching approach of the Tetracorder system (SCSMT) has been widely used for material identification. However, the SCSMT approach is performed directly on the continuum-removed spectra. It only catches the shape features on a large scale, missing the fine structures on spectral curves. To consider this issue, a new spectral shape matching approach is proposed, which employs the first-order and second-order spectral derivatives to capture the fine structures on spectra. A new metric is designed to balance the contributions of spectral curves, their first-order and second-order derivatives. To evaluate the performance of the proposed approach, it is applied to synthesized spectra as well as to identify oil spills from two practical hyperspectral images. The experimental results show that the proposed approach can improve the detection performance of the SCSMT approach.