Comparative analysis of PCA and KPCA on paddy growth stages classification

Hyperspectral image is capable to distinguish paddy growth stages with classification methods. Hyperspectral has disadvantages. One of the disadvantages is hyperspectral image has high dimensionality that can cause curse of dimensionality. In this paper, PCA and Kernel PCA are used to reduce the dimension of hyperspectral data. The objective in this research is to analyze the effect of using dimension reduction techniques on hyperspectral data on paddy growth stages classification. The result will show the effect of dimension reduction techniques whether it is capable to improve the classification accuracy and execution time.