Robust Matched Filters for Target Detection in Hyperspectral Imaging Data
Abstract
Most detection algorithms for hyperspectral imaging applications assume a targetwith a perfectly known spectral signature. In practice, the target signature is either imperfectly measured (target mismatch) and/or it exhibits spectral variability. The objective of this paper is to introduce a robust matched lter that takes the uncertainty and/or variability of target signatures into account. It is shown that, if we describe this uncertainty with an ellipsoid in the spectral space, we can design a matched lter that provides a response of the same magnitude for all spectra within this ellipsoid. Thus, by changing the size of this ellipsoid, we can control the "spectral selectivity" of the matched lter. The ability of the robust matched lter to deal effectively with target mismatch and spectral variability is demonstrated with hyperspectral imaging data from the HYDICE sensor.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2007
- Accession Number
- ADA489053
Entities
People
- Dimitris G. Manolakis
- J. Jacobson
- R. Lockwood
- T. Cooley
Organizations
- Massachusetts Institute of Technology