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- W4327563533 abstract "Hyperspectral images have a large number of contiguous spectral bands, many of which can be redundant. To reduce both the redundancy of bands and the storage space requirement, Principal Component Analysis (PCA) is widely used, which orders the Principal Components (PCs) based on global variance and removes any information that is not useful for compression, thus some amount of class discriminant information is lost during the transformation. This poses a challenge for the classification of hyperspectral images. To address this problem, a Class Information-based Principal Component Analysis (CI-PCA) has been proposed which retains the class discriminant information for classification purposes. The proposed algorithm involves the following steps: 1) selection of training pixels or areas of each defined class to constitute the training data/image cube, 2) computation ofPCA for the training data of each class separately, and 3) superimposition of the PCA of each class to construct the CI-PCA image, which can contain the class information of all defined classes. The superiority of the proposed CI-PCA for classification purposes has been demonstrated using a recently proposed Wrapper method with Naive Bayes classifier as a wrapper for two widely used and real hyperspectral datasets, namely Washington DC Mall (WDC-M) and Salinas-A." @default.
- W4327563533 created "2023-03-17" @default.
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- W4327563533 date "2023-01-27" @default.
- W4327563533 modified "2023-09-23" @default.
- W4327563533 title "Class Information-based Principal Component Analysis Algorithm for Improved Hyperspectral Image Classification" @default.
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- W4327563533 doi "https://doi.org/10.1109/migars57353.2023.10064597" @default.
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