A comparative study of remotely sensed data classification using principal components analysis and divergence

This paper investigates the principal components analysis (PCA) and divergence for transforming and selecting data bands for multispectral image classification. As the principal components are independent of one another, a color combination of the first three components can be useful in providing maximum visual separability of image features. Therefore, principal components analysis is used to generate a new set of data. Divergence, a measurement of statistical separability, is employed as a method of feature selection to choose the optimal m-band subset from the n-band data for use in the automated classification process. Classification accuracy assessment is carried out using large scale aerial photographs. Classification results on the Landsat Thematic Mapper (TM) data show that PCA is a more effective approach than divergence.