A Fuzzy ISODATA Approach Combing Hidden Markov Random Field Model for High Resolution Remote Sensing Image Segmentation

Fuzzy ISODATA (FISODATA) algorithm inherits the expansibility of FCM and self-organization of ISODATA and can obtain the number of classes and good clustering results simultaneously. Consequently, FISODATA has been already applied in many image processing fields. For image segmentation tasks, its objective function does not consider the effects from neighbor pixels, so it is sensitive for noises. Besides, the splitting and merging operations of FISODATA need parameters selected manually which may cause local optimization result. In this paper, Hidden Markov Random Field FCM (HMRF-FCM) is brought into the ISODATA framework, the adaptive splitting and merging operations are designed for the purpose of formulating HMRF-FCM ISODATA (HMRF-FISODATA) algorithm. The proposed algorithm can not only obtain the correct number of classes automatically but also overcomes the shortcoming of manually selecting parameters and seriously noise effect of FISODATA, as a result, it obtains segmentation results accurately.