Hyperspectral detection algorithms: Use covariances or subspaces?

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7457, Issue , 2009, Pages

  Manolakis, D ^a   Lockwood, R ^b   Cooley, T ^b   Jacobson, J ^c  

^a MIT LINCOLN LABORATORY   (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

^c WRIGHT PATTERSON AIR FORCE BASE   (United States)

Author keywords

Background characterization; Hyperspectral imaging; Statistical modeling; Target detection

Indexed keywords

BACKGROUND CHARACTERIZATION; BACKGROUND CLUTTER; DETECTION ALGORITHM; DOMINANT MODE REJECTIONS; HYPERSPECTRAL; HYPERSPECTRAL BACKGROUNDS; HYPERSPECTRAL IMAGING; HYPERSPECTRAL IMAGING DATUM; LINEAR MIXTURE MODELS; SPECTRAL COVARIANCE MATRIX; STATISTICAL MODELING; SUBSPACE MODELS; TARGET DETECTION;

ALGORITHMS; ATMOSPHERICS; CLUTTER (INFORMATION THEORY); COVARIANCE MATRIX; SIGNAL DETECTION; SPECTROMETRY; TARGET DRONES;

RADAR TARGET RECOGNITION;

EID: 70350146726     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.828397     Document Type: Conference Paper

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