Automatic mixed pixel detection using a new hybrid Cellular automata approach on satellite image

Mixed-pixels classification in land-cover regions is a challenging task in remote sensing imagery. To classify mixed-pixels, vagueness is always the main characteristic by handling uncertainty. We propose a hybrid approach for pixel classification using Rough sets and Cellular automata models to solve this problem. Multiple belongingness and vagueness among data can be handled efficiently using Rough set theory and is appropriate for detecting arbitrarily-shaped clusters in satellite images. We propose a rough-set based automatic heuristically decision-rule generation algorithm to obtain initial set of clusters. As a discrete, dynamical system, cellular automaton comprises of uniformly interconnected cells with states. In the second phase of our method, we utilize a 2-dimensional cellular automaton to prioritize allocations of mixed pixels among overlapping land cover regions. We experiment our algorithm on Ajoy river catchment area. The segmented regions are compared with well-known FCM and K-Means methods and the ground truth knowledge, which shows superiority of our new approach.