Matte based generation of land cover maps

A novel supervised technique for the generation of spatially consistent land cover maps based on class-matting is presented in this paper. This method takes advantage of both standard supervised classification technique and natural image matting. It adaptively exploits the spatial contextual information contained in the neighborhood of each pixel through the use of image matting to reduce the incongruence inherent in pixel-wise, radiometric classification of multi-spectral remote sensing data, providing a more spatially homogeneous land-cover map besides yielding a better accuracy. In order to make image matting possible for N-class land cover map generation, we extend the basic alpha matting problem into N independent matting problems, each conforming to one particular class. The user input required for the alpha matting algorithm in terms of initially identifying a few sample regions belonging to a particular class (known as the foreground object in matting) is obtained automatically using the supervised ML classifier. Experimental results obtained on multispectral data sets confirm the effectiveness of the proposed system.