An Improved Weighted Fuzzy C-Means Algorithm with Spatial Information for Remote Sensing Image Segmentation

In this paper,we propose an improved spatial-weighed fuzzy C-means algorithm since the traditional fuzzy C-means algorithm is more sensitive to the initial cluster centers than other commonly deployed algorithms,such as the FCM,SFCM algorithms.BP neural network algorithms are used to train samples and obtain an initial membership matrix,thus increasing the reliability of the initial cluster centers.Since spatial data have pattens of spatial auto-correlation,the neighboring pixels will contribute to the center pixel with different weights to robustly handle noises.Segmentation experiments were conducted using SPOT 2.5 meters remote sensing images to verify the effectiveness of our algorithm.In comparison with FCM,SFCM algorithms,the experimental results show that the proposed method obtains better results.