International Journal of Advancements in Research & Technology, Volume 2, Issue 8, August-2013 103 ISSN 2278-7763 Copyright © 2013 SciResPub. IJOART Relational Features of Remote Sensing Image Classification using Effective K-Means Clustering T. Balaji 1 , M. Sumathi 2 1 Assistant Professor, Dept. of Computer Science, Govt. Arts College, Melur, India 2 Associate Professor, Dept. of Computer Science, Sri Meenakshi Govt. College for Women, Madurai, India 1 bkmd_gacm@rediffmail.com, 2 sumathivasagam@gmail.com ABSTRACT The feature based classification of remotely sensed image is used to assign corresponding levels with respect to groups with homogeneous characteristics, with the aim of discriminating multiple objects from each other within the image. Every level of an image is called class. This will be executed on the basis of spectral or spectrally defined features such as density, texture and many other things in the feature space. This paper focuses remote sensing image classification of color feature based using k-means clustering method. K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest centroid. When no point is pending, the first step is completed and we need to re- calculate k new centroids of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new centroid. A loop has been generated. As a result of this loop we may notice that the k centroids change their location step by step until no more changes are done. Here we introduce several widely used algorithms that consolidate data by clustering or grouping and then present a suitable method is remote sense application based k-means cluster algorithm. It is possible to reduce the computational cost and gives a high discriminative power of regions present in the image. Keywords: Classification, Clustering, Unsupervised, Segmentation and Partitioning 1 INTRODUCTION Remote sensing image classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. Depending on the interaction between the analyst and the computer during classification, there are two types of classification: supervised and unsupervised. The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object or type of land cover these features actually represent on the ground. A broad group of digital image processing techniques of remote sensing application explains the image classification techniques are most generally applied to the spectral data of a single- date image or to the varying spectral data of a series of multidimensional images. Clustering involves a set of point into non-overlapping groups or points; where points in a cluster are more similar to one another than points in other clusters. The term more similar, when applied to clustered points, usually means closer by some measure of proximity. When a dataset is clustered, every point is assigned to some cluster and every cluster can be characterized by a single reference point, usually take an average of the points in the cluster. Any particular division of all points in a dataset into clusters is called a partitioning. Clustered data require considerably less storage space and can be manipulated more quickly than the original data. The value of particular clustering method will depend on how relatively the reference points represent the data as well as how fast the program runs. A general classification procedure of remotely sensed image is shown in the following fig. 1. Fig. 1. General Procedure for Classification IJOART