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

fYear

2012

fDate

Sept. 30 2012-Oct. 3 2012

Firstpage

2669

Lastpage

2672

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2012 19th IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1522-4880

Print_ISBN

978-1-4673-2534-9

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2012.6467448

Filename

6467448