Detection in hyperspectral imagery

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 3372, Issue , 1998, Pages 188-198

  Schweizer, S M ^a   Moura, J M F ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Gauss Markov random field; Hyperspectral; Least squares estimation; Maximum likelihood detector

Indexed keywords

ALGORITHMS; AUTOMATIC TARGET RECOGNITION; IMAGE ANALYSIS; IMAGE SENSORS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; SENSOR DATA FUSION; SIGNAL DETECTION;

GAUSS-MARKOV RANDOM FIELD MODEL; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL SENSOR DATA; LEAST SQUARE DETECTOR; MAXIMUM LIKELIHOOD DETECTOR;

RADAR CLUTTER;

EID: 0032404291     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.312600     Document Type: Conference Paper

References (14)

1. Adaptive spatial/spectral detection of subpixel targets with unknown spectral characteristics
2. Automatic target detection and recognition in multiband imagery: A unified ML detection and estimation approach
3. A noise subspace protection approach to target signature detection and extraction in an unknown background for hyperspectral images
4. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution
5. Two-dimensional discrete Markovian fields
6. Noncausal gauss-markov random fields and parameter structure and estimation
7. Adaptive detection of sub-pixel targets using multi-band frame sequences
8. Recursive structure of noncausal Gauss-Markov random fields
9. Detection in hyperspectral imagery