The Relationship Between Detection Algorithms for Hyperspectral and Radar Applications
Abstract
Hyperspectral data consists of hundreds of contiguous radiometric measurements collected passively from each pixel in a scene. Detection capitalizes on exploiting the difference between target and background spectral signatures. Many detection methods in hyperspectral processing employ signal models commonly used in radar even though it is an active sensor. Starting from a common signal model, we discuss adaptive detection algorithms for hyperspectral data by outlining fundamental similarities and differences with radar. We demonstrate detection using hyperspectral data through experiments with real data and discuss the fundamental applicability of adaptive radar signal models to detection in hyperspectral processing.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2000
- Accession Number
- ADA405394
Entities
People
- Dimitris G. Manolakis
- Nirmal Keshava
- Stephen M. Kogon
Organizations
- Massachusetts Institute of Technology