Algorithms for sub-pixel analysis of remotely, sensed hyperspectral images

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In this research, we developed several algorithms for sub-pixel analysis of land cover for remotely sensed multispectral image. Several techniques of sub-pixel 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.We developed the algorithm by which a hyperspectral image is analyzed as follows. At 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 the method by numerical simulation and applied it to remotely sensed multispectral images.