28 March 2005 Reduced data projection slice image fusion using principal component analysis
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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.
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Vahid R. Riasati, Vahid R. Riasati, Hui Zhou, Hui Zhou, "Reduced data projection slice image fusion using principal component analysis", Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); doi: 10.1117/12.604561; https://doi.org/10.1117/12.604561
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