  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.
