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Exploration of integrated visible to near-, shortwave-, and longwave-infrared (full range) hyperspectral data analysis

Visible to near-, shortwave-, and longwave-infrared (VNIR, SWIR, LWIR) hyperspectral data were integrated using a variety of approaches to take advantage of complementary wavelength-specific spectral characteristics for improved material classification. The first approach applied separate minimum noise fraction (MNF) transforms to the three regions and combined only non-noise transformed bands. A second approach integrated the VNIR, SWIR, and LWIR data before using MNF analysis to isolate linear band combinations containing high signal to noise. Spectral endmembers extracted from each integrated dataset were unmixed and spatially mapped using a partial unmixing approach. Integrated results were compared to baseline analyses of the separate spectral regions. Outcomes show that analyzing across the full VNIR-SWIR-LWIR spectrum improves material characterization and identification.