Dimensionality Reduction of Hyperspectral Data – A Case Study
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Abstract
At present hyperspectral image investigation has become main exploration territory in remote sensing. Hyperspectral sensors are capable of detecting a wide spectrum of electromagnetic spectrum from Ultraviolet, Visible and Infra-Red and produce images with hundreds of continuous bands, in the form of a image cube. Processing of these high dimensional hyperspectral images using conventional image processing techniques such as classification, recognition etc., without reducing dimensionality is a very tedious task. Hence in this research dimensional reduction was considered using PCA, Incremental PCA, truncated SVD and their fitness to various datasets was discussed in this paper.
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