Subpixel estimation of land cover in a remotely sensed image using spectral information of surrounding pixels

Several techniques of subpixel analysis for remotely sensed image have been developed which estimate the proportion of components of land cover in a pixel. However, when the available training data do not correctly represent the spectral characteristics of the categories in the pixel, large errors may appear in the results of estimation. In this paper, we propose a semi-supervised method of subpixel estimation of land cover for remotely sensed multispectral image. First we provide small size of initial training data and determine pure pixels in the image. In the next step, component spectra are adaptively estimated for each mixed pixel using the surrounding pure pixels. Then the proportions of components in the mixed pixels are estimated based on the determined component spectra. We confirmed the validity of this method by numerical simulation and applied it to a remotely sensed multispectral image.