Relational Features of Remote Sensing Image Classification using Effective K-Means Clustering

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 recalculate 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.