<title>Principal component analysis of remote sensing imagery: effects of additive and multiplicative noise</title>

Abstract

The potential of high-resolution radar and optical imagery for synoptic and timely mapping in many applications is well- known. Numerous methods have been developed to process and quantify useful information from remotely sensed images. Most image processing techniques use texture based statistics combined with spatial filtering to separate target classes or to infer geophysical parameters from pixel radiometric intensities. The use of spatial statistics to enhance the information content of images, thereby providing better characterization of the underlying geophysical phenomena, is a relatively new technique in image processing. We are currently exploring the relationship between spatial statistical parameters of various geophysical phenomena and those of the remotely sensed image by way of principal component analysis (PCA) of radar and optical images. Issues being explored are the effects of noise in multisensor imagery using PCA for land cover classifications. The differences in additive and multiplicative noise must be accounted for before using PCA on multisensor data. Preliminary results describing the performance of PCA in the presence of simulated noise applied to Landsat Thematic Mapper (TM) images are presented.