Super-resolution mapping based on the supervised fuzzy c-means approach
Super-resolution mapping (SRM) is a process that provides land-cover spatial distribution with a spatial resolution finer than the size of a remotely sensed image pixel. Usually, the fraction images generated from soft classifiers are used as constraints in SRM, making the accurate estimation of fraction images an important task in SRM. Supervised fuzzy c-means (sFCM), which belongs to the fuzzy-set technology commonly applied for unmixing mixed pixels, is capable of providing accurate estimates of fraction images used for SRM.