Fuzzy C-Means clustering algorithm for classification of sea ice and land cover from SAR images

A two-step Fuzzy C-Means (FCM) clustering algorithm was presented in this paper. In the first step a region-growing algorithm was utilised to make the data set over split, and the data set was reconstructed by using the mean values of the segments. In the second step a traditional FCM clustering algorithm was realised to segment the reconstructed data set. In order to get the physical classes, a simple data training or the use of prior knowledge was required. The mean values of each class were obtained from the data training. Then the physical classes were identified through a simple distance measure. In order to improve the classification accuracy, a post-processing was developed by using a majority filter based on the sizes of objects and the context information. The algorithm was applied to two different applications, classification of sea ice and land cover from ERS-1/2 Synthetic Aperture Radar (SAR) images. In the sea ice case the SAR PRI images and the first order statistical parameter were used. The algorithm was also compared with a statistical classification method in this case. In the land cover case the SAR SLC images, the first order statistical parameter and the interferometric coherence information was used. Especially a set of proper logical calculation rules were used to determine the physical classes. The experiments have shown that the presented algorithm had a better performance and was more automatic in the case of multi-channel classification from SAR images than the statistical model.