Research on image reconstruction based and pixel unmixing based sub-pixel mapping methods

The sub-pixel mapping technique, which can provide a fine-resolution map of class labels, has attracted more and more attention in recent years. Generally speaking, there are two kinds of methods used to realize the sub-pixel labeling. The first kind are image reconstruction based methods, which first improve the spatial resolution of an image by the super-resolution technique, and then perform a hard classification on the super-resolved image. The second kind are pixel unmixing based methods, where the sub-pixel mapping is implemented based on the results of image unmixing. In this paper, we present a sparse representation method and a back-propagation (BP) neural network method for image reconstruction based and pixel unmixing based mapping, respectively. The advantages and disadvantages of both kinds of methods are analyzed and discussed.