Weighted Sum of Segmented Correlation: An Efficient Method for Spectra Matching in Hyperspectral Images

TL;DR

This paper presents a novel spectral matching method that segments spectra and weights correlations to improve material identification in hyperspectral images from Earth and Mars.

Contribution

It introduces the Weighted Sum of Segmented Correlation method, enhancing spectral matching accuracy by emphasizing positive correlations and penalizing negatives.

Findings

Effective mineral identification demonstrated on Earth and Martian hyperspectral data.

Improved matching accuracy over traditional correlation methods.

Method efficiently handles spectral segment variations.

Abstract

Matching a target spectrum with known spectra in a spectral library is a common method for material identification in hyperspectral imaging research. Hyperspectral spectra exhibit precise absorption features across different wavelength segments, and the unique shapes and positions of these absorptions create distinct spectral signatures for each material, aiding in their identification. Therefore, only the specific positions can be considered for material identification. This study introduces the Weighted Sum of Segmented Correlation method, which calculates correlation indices between various segments of a library and a test spectrum, and derives a matching index, favoring positive correlations and penalizing negative correlations using assigned weights. The effectiveness of this approach is evaluated for mineral identification in hyperspectral images from both Earth and Martian surfaces.