Adaptive subpixel estimation of land cover in a remotely sensed multispectral image

Land surface corresponding to a pixel of remotely sensed image does not necessarily consist of only one category of objects. Several techniques of subpixel analysis 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. The method of unsupervised estimation of component spectra has been presented to solve this problem. In this paper we present a method which apply the unsupervised analysis technique to subpixel estimation of land cover in an image in which spectral characteristics change with the location of the objective area. After partitioning the image into blocks, the number of categories and their component spectra are estimated in each block. Then the proportion of category are estimated for each pixel using the component spectra derived in the block. We confirmed the validity of this method by numerical simulation.