PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images

Abstract

In this chapter an application of PCA, kernel PCA with their modified versions are discussed in the field of dimensionality reduction of hyperspectral images. Hyperspectral image cube is a set of images from hundreds of narrow and contiguous bands of electromagnetic spectrum from visible to near-infrared regions, which usually contains large amount of information to identify and distinguish spectrally unique materials. In hyperspectral image analysis, reducing the dimensionality is an important step where the aim is to discard the redundant bands and make it less time consuming for classification. Principal component analysis (PCA), and the modified version of PCA, i.e., segmented PCA are useful for reducing the dimensionality. A brief detail of these PCA based methods in the field of hyperspectral images with their advantages and disadvantages are discussed here. Also, dimensionality reduction using kernel PCA (one of the non linear PCA) and its modification i.e., clustering oriented kernel PCA in this field are elaborated in this chapter. Advantages and disadvantages of all these methods are experimentally evaluated over few hyperspectral data sets with different performance measures.