In hyperspectral data analysis, blind separation of the target and background can be considered as a pre-processing step in the target detection process. Blind Source Separation (BSS) techniques can be used when there is no prior information on the scene for image understanding. Previously, Principal Component Analysis and Independent Component Analysis methods are used as blind techniques for the analysis of hyperspectral data. In this study, we propose a blind analysis methodology based on Canonical Correlation Analysis (CCA) for the analysis of hyperspectral data sets. CCA is a multivariate method of analysis for the exploration of the data structures which extremize the correlations between two data sets. The hyperspectral data analyzed in this study is the HyMap sensor data which is available in the Target Detection Blind Test website. We produce two data sets out of the HyMap data cube which are later subjected to CCA. In the creation of these data sets, two different approaches are used. In first case, the HyMap data cube is simply divided into two sub-cubes by simple spectral separation. As another approach, the second data cube is derived from the HyMap data by a spatial filtering. In both cases, two data sets are analyzed via CCA and canonical variates of these data sets are obtained. The scene components are obtained from images expressed by the canonical variates obtained via CCA. The CCA methodology and its use as a blind analysis tools is presented on the HyMap data.

Date Published: 18 May 2013
PDF: 8 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431K (18 May 2013); doi: 10.1117/12.2016094

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