A Fuzzy Kohonen Local Information C-Means Clustering for Remote Sensing Imagery

Abstract

ABSTRACTThis paper presents a neuro fuzzy clustering algorithm, Fuzzy Kohonen Local Information C-Means (FKLICM), for classification of remote sensing images. The proposed algorithm is a hybridization of the conventional Kohonen clustering network and Fuzzy Local Information C-Means (FLICM) to produce a much more efficient and accurate clustering algorithm. The proposed algorithm first forms a fused image with three Multispectral bands and pan band of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using the Brovey transform. The fused image is a three band image with higher resolution and better visual perception. The fused image is reduced to a one-dimensional image using principal component analysis (PCA). The FKLICM algorithm is applied on the PC-1 image to classify the remote sensing image into different land cover types. Integrating the neural network with a fuzzy system combines the advantages and overcomes the limitations of both technologies. The experimental results of the proposed algorithm are compared with two other algorithms, FCM and GIFP-FCM. The classification results and accuracy assessment show that FKLICM yields better results than the other methods.