Title :
Performance evaluation of cluster-based hyperspectral target detection algorithms
Author :
Pieper, Michael ; Manolakis, Dimitris ; Truslow, Eric ; Cooley, Thomas ; Lipson, S.
Author_Institution :
MIT Lincoln Lab., Lexington, MA, USA
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
Detection of targets in background clutter using hyperspectral imaging sensors, is a problem of great practical interest [1]. This paper addresses some practical problems related to the adaptive estimation of clutter models and their effects on the performance of matched-signature detection algorithms. More specifically, we compare clutter estimation algorithms using spatially-local adaptation or spectral clustering to deal with the nonstationarity of hyperspectral backgrounds.
Keywords :
adaptive estimation; clutter; geophysical image processing; hyperspectral imaging; image matching; image sensors; object detection; pattern clustering; adaptive estimation; background clutter; cluster-based hyperspectral target detection algorithm; clutter estimation algorithm; clutter model; hyperspectral background nonstationarity; hyperspectral imaging sensor; matched-signature detection algorithm; spatially-local adaptation; spectral clustering; Clutter; Computational modeling; Detectors; Hyperspectral imaging; Materials; Signal processing algorithms; CFAR processing; Hyperspectral imaging; clustering; matched filtering; target detection;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6467448
