Weighted Sum of Segmented Correlation: An Efficient Method for Spectra Matching in Hyperspectral Images
This is an incremental improvement for researchers in hyperspectral imaging, focusing on more efficient spectra matching for material identification.
The study tackled the problem of material identification in hyperspectral images by introducing the Weighted Sum of Segmented Correlation method, which calculates correlation indices between segments of a library and test spectrum with weighted penalties, and evaluated its effectiveness for mineral identification on Earth and Martian surfaces, though no concrete numbers were provided.
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.