Dimensionality Reduction of Hyperspectral Data – A Case Study - et. al., Vamshi Krishna Munipallea, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
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.