The influence of principal component analysis on the spatial structure of a multispectral dataset

Multispectral remote sensing images often have extensive interband correlation. As a result, the images may contain similar information and have similar spatial structure. Principal component analysis (PCA) is a technique for removing or reducing the duplication or redundancy in multispectral images and for compressing all of the information that is contained in an original n-channel set of multispectral images into less than n channels or, more specifically, to their principal components. These are then used instead of the original data for image analysis and interpretation. The principal components are ranked in terms of the amount of variance that they explain. A consequence of ranking in this way is that the resulting principal components show a markedly different spatial structure from one another. This effect can be problematical, for example, when studying landscape ecology, where understanding the interactions between elements of the landscape structure as manifest in remote sensing images and environmental processes is of primary importance. Although the difference in spatial structure of an image after applying PCA and its influence on potential applications have been known for some time, it does not appear to have been studied explicitly. Accordingly, the aim of this paper was to examine the implications of applying PCA for the spatial structure and content of multispectral remote sensing images using parts of a Landsat Thematic Mapper (TM) frame of northern Sardinia, Italy. The results show that, due to the significant influence of PCA on the spatial structure and content of remote sensing images, the resulting principal components have a spatial structure and content that differ markedly from one another and from the original images. As a result, extreme care is necessary when applying PCA to remote sensing images and interpreting the results.