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Proceedings Paper

Blind analysis of multispectral and polarimetric data via canonical correlation analysis
Author(s): Özgür Murat Polat; Yakup Özkazanç
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Paper Abstract

In blind scene analysis, the aim is to obtain information about background and targets without any prior information. Blind methods can be considered as pre-processing steps for scene understanding. By means of blind signal separation methodologies, anomalies can be detected and these anomalies can be exploited for target detection. There are many imaging sensor systems which uses different properties of the emittance or the reflectance characteristics of the scene components. Spectral reflectance properties are related to the material composition and these multispectral characteristics can be exploited for detection, identification and classification of the scene components. As the light scattered from the scene elements shows polarization, polarized measurements can be used as extra features. Multispectral and polarimetric images of a scene provide information to some level and this information can be used to get further information on the scene and to facilitate detection. In this study, spectral and polarimetric images of a scene are analyzed via Canonical Correlation Analysis (CCA) which is a powerful multivariate statistical methodology. Multispectral and polarimetric data (spectro-polarimetric data) are treated as two different sets. Canonical variants obtained by CCA give different scene components such as background elements and some man-made objects. The linear relationship of the polarimetric and multispectral data of the same scene is also obtained by CCA.

Paper Details

Date Published: 18 May 2013
PDF: 7 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430T (18 May 2013); doi: 10.1117/12.2016047
Show Author Affiliations
Özgür Murat Polat, ASELSAN Inc. (Turkey)
Yakup Özkazanç, Hacettepe Univ. (Turkey)


Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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