Improved Sub-pixel Material Identification with Spline-Based Spectral Smoothing

Hyperspectral imagers allow for the identification of materials of interest (MOI) in a scene using spectroscopic analysis. Material identification is made more difficult when the MOI fills a fraction of the pixel. The resulting pixel spectrum is a linear mixture of the MOI and its surrounding background with weights/abundances based on the fraction of each material within the pixel. Some identification methods utilize pixel unmixing to match a library spectrum to the suspected MOI. The resulting quality of fit depends on how closely the library spectrum matches that of the MOI. Further analysis is accomplished by removing the background portion, and comparing the normalized MOI portion to the library spectrum. When the library spectrum corresponds to the MOI, the two spectra should visually match, with the exception of noise. Often, spectral smoothing is required to improve the match as the SNR of the MOI portion decreases with its abundance. We propose a spline-based smoothing method to reduce noise error greatly while maintaining the fine spectral features used to distinguish between spectrally similar materials. The main practical difficulty of spline smoothing lies in setting the parameter which determines the amount of smoothing. We show that automatically setting the smoothing parameter, such that the roughness of the smooth MOI portion matches the library spectrum, prevents over smoothing and improves identification.