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Proceedings Paper

Reduced data projection slice image fusion using principal component analysis
Author(s): Vahid R. Riasati; Hui Zhou
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Paper Abstract

In this paper we discuss the utilization of Principal Component Analysis, PCA, with projection slice synthetic discriminant function (PSDF) filters to reduce a data set that represents images from different sensor systems in order to extract relative information and features from the image set. The PCA helps to emphasize the differences in each of the training images in a given class. These differences are encoded into the PSDF filters. The PSDF filters provide a premise for data fusion by utilization of the projection-slice theorem. The PSDF is implemented with a few training images generated from the PCA, containing relevant information from all of the training images. The data in the principle components that are used to represent the entire data set can be emphasized by conditioning the eigen-values of the basis vectors used to corroborate important data packets in the entire data set. The method of data fusion, and preferred data emphasis in conjunction with the PST is discussed and the fused images are presented.

Paper Details

Date Published: 28 March 2005
PDF: 15 pages
Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); doi: 10.1117/12.604561
Show Author Affiliations
Vahid R. Riasati, Science Applications International Corp. (United States)
Hui Zhou, California State Polytechnic Univ. (United States)

Published in SPIE Proceedings Vol. 5813:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005
Belur V. Dasarathy, Editor(s)

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