Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image. Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy-superpixels, is proposed for reducing the generation of mixed superpixels. In fuzzy-superpixels, not all pixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assign them to improper superpixels. Second, a new algorithm is proposed to generate fuzzy-superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed fuzzy superpixels algorithm. Experimental results demonstrate the superiority of the proposed fuzzy-superpixels algorithm over several state-of-the-art superpixels algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.