Distance Metrics and Band Selection in Hyperspectral Processing with Applications to Material Identification and Spectral Libraries
Abstract
In this report, we investigate and exploit the properties of distance metrics in hyperspectral processing to achieve superior algorithm performance as well as dimension reduction. Distance metrics are mathematical operators that provide a scalar measure of similarity for two hyperspectral (vector) signals, and they are at the nucleus of many application algorithms. The similarity between two signals, however, can be measured by various means, and different distance metrics offer distinct notions of similarity. Consequently, a thorough understanding of the mathematical and physical properties of distance metrics is crucial to the accurate and efficient processing of hyperspectral data.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 18, 2002
- Accession Number
- ADA409023
Entities
People
- N. Keahava
Organizations
- Massachusetts Institute of Technology