In this study, Linear Spectral Mixture Model (LSMM) is used to estimate the proportion of water in boundary pixels. Firstly by applying an unsupervised clustering, the water body is identified approximately and a buffer area considered ensuring the selection of entire boundary pixels. Then LSMM is applied on this buffer region to estimate the fractional maps. However, resultant output of LSMM does not provide a sub-pixel map corresponding to water abundances. To tackle with this problem, Pixel Swapping (PS) algorithm is used to allocate sub-pixels within mixed pixels in such a way to maximize the spatial proximity of sub-pixels and pixels in the neighborhood.
The water area of two segments of Tagliamento River (Italy) are mapped in sub-pixel resolution (10m) using a 30m Landsat image. To evaluate the proficiency of the proposed approach for sub-pixel boundary mapping, the image is also classified using a conventional hard classifier. A high resolution image of the same area is also classified and used as a reference for accuracy assessment. According to the results, sub-pixel map shows in average about 8 percent higher overall accuracy than hard classification and fits very well in the boundaries with the reference map.