Clustering of Remote Sensing Standard Datasets Using Kernelised Fuzzy C Means

Abstract: Satellite imaging is being the most attractive source of information for the governmental agencies and the commercial companies in last decade. The quality of the images is very important especially for the military or the police forces to pick the valuable information from the details. Satellite images may have unwanted signals called as noise in addition to useful information for several reasons such as heat generated electrons, bad sensor, wrong ISO settings, vibration and clouds. There are several image enhancement algorithms to reduce the effects of noise over the image to see the details and gather meaningful information. Satellite images are acquired with remote sensing. Remote sensing is the science and art of obtaining information about an object or area through a device that is not in contact with the object or the area under investigation. Satellite Images referred as hyper spectral images are the most used images in remote sensing and are of more interest to find out the classification of objects in those images. The classification can give us the important factors like vegetation, buildings, roads and more. The classification can be done by using Image segmentation via various clustering algorithms where Clustering is the process of grouping a set of objects into classes of similar objects. In this work FCM based algorithms are investigated and a Kernel based FCM is proposed and compared with existing generalized FCM.

Keywords: Clustering, C-Means, Fuzzy C-means, Generalized Fuzzy C-Means, Kernel Fuzzy C-means, Remote Sensing.