DISCIPLINARY IMPROVEMENTS: (HS-SPECTRA) Hyperspectral Standardizing and Sharing Possibilit — NSF Award to New York University (NY,
Accurate interpretation of hyperspectral data depends on the availability of reference spectra: measurements of known materials compiled into spectral libraries. Such libraries support both direct classification and machine learning applications. When combined with on-site hyperspectral imaging, they have proven effect
| Award title | DISCIPLINARY IMPROVEMENTS: (HS-SPECTRA) Hyperspectral Standardizing and Sharing Possibilit |
|---|---|
| Award ID | 2531997 |
| Awardee | New York University |
| City | NEW YORK |
| State | NY |
| Amount obligated | $600,000 |
| Principal investigator | Debra Laefer |
| Program | NSF Public Access Initiative, SSA-Special Studies & Analysis |
| Start date | 10/01/2025 |
| Abstract | Accurate interpretation of hyperspectral data depends on the availability of reference spectra: measurements of known materials compiled into spectral libraries. Such libraries support both direct classification and machine learning applications. When combined with on-site hyperspectral imaging, they have proven effective across a variety of domains including heritage conservation, homeland security, hydrology, and geology. Urban conditions, however, present unique challenges to spectral data co |
| Source | NSF Awards |
$799/mo
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