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- W2083047492 abstract "In case of spatial multi spectral images, such as remotely sensed earth cover, there could be many classes in one entire frame covering a large spatial stretch, because of which meaningful dimensionality reduction cannot perhaps be realizable without trading off with the quality of classification. However most often one would encounter in such images, presence of only a few classes in a small neighborhood, which would enable to devise a very effective dimensionality reduction around that small neighborhood identified as a block. Based on this theme a new method for dimensionality reduction is proposed in this paper. The method proposed divides the image into uniform non-overlapping windows/blocks. The few features that are essential in discriminating classes in a window are identified. Clustering is performed independently on each of the blocks with the reduced set of features. These clusters in the blocks are later merged to obtain an overall classification of the entire image. The efficacy of the method is corroborated experimentally." @default.
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- W2083047492 date "2004-12-01" @default.
- W2083047492 modified "2023-09-26" @default.
- W2083047492 title "Content driven dimensionality reduction at block level in the design of an efficient classifier for spatial multi spectral images" @default.
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- W2083047492 doi "https://doi.org/10.1016/j.patrec.2004.07.008" @default.
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