Automated Change Detection in Land-cover Pattern Using Region Growing Segmentation and Fuzzy Vector

This study has utilized a spatial region growing segmentation and a classification using fuzzy membership vectors to detect the changes in the images observed at different dates. Consider two co-registered images of the same scene, and one image is supposed to have the class map of the scene at the observation time. The method performs the unsupervised segmentation and the fuzzy classification for the other image, and then detects the changes in the scene by examining the changes in the fuzzy membership vectors of the segmented regions in the classification procedure. The algorithm has evaluated with simulated images and has then applied to a real scene of the Korean Peninsula using the KOMPSAT-1 EOC images. In the experiments, the proposed method has shown a great performance for detecting changes in land-cover.