Abstract— Unsupervised classification creates clusters by grouping pixels based on the reflectance properties of pixels. This paper presents a new approach to classify multi-spectral remotely sensed image using pixels’ density in N-dimensional scatterplot. It first finds the densely populated clusters in N- dimensional scatter plot and then finds gravity centers of these densely populated clusters. Later, multispectral image is classified using minimum distance to gravity classifier. At the beginning of classification, this approach neither makes extreme assumption of considering each pixel as a different cluster nor goes to the other extreme by considering all the pixels in a single cluster. It follows the middle path by making assumption of some pixels as gravity centers of different clusters before classifying the image. It creates clusters of equal size in N-dimensional scatter plot and picks up the densely populated clusters. All these clusters are recursively iterated for self-adjustment of gravity centers using mathematical algorithm. The approach uses gravitational force for merging two nearby clusters. Here, gravity center of a cluster is calculated by summing up the spectral bands’ values and dividing it by number of pixels within that cluster. When two nearby clusters are merged, their gravity centers are also adjusted accordingly. This provides the most densely populated clusters and their gravity centers. Now, these gravity centers can be used to classify the remotely sensed image using minimum distance to gravity center classifier. Index Terms— Clustering, Multi-Spectral, Remote Sensing, Scatter Plot, Unsupervised Classification, I. INTRODUCTION he classification is the grouping of pixels with common characteristics. It divides the feature space into several classes. The remotely sensed images can be classified using unsupervised classifier, supervised classifier or object-based classifier. Supervised classification needs training of data, whereas unsupervised classifier needs no training of data. Unsupervised classifier uses mathematical algorithm for classifying the data. The object-based classification is based on set of similar pixels called objects. The reflectance signals are captured as analog signals and converted to digital numbers. These digital number (DN) values are also called grey-level values. The number of grey This paper was submitted on 3 rd August 2015. This work was not supported in part or in full from anywhere. The author is working as Senior Consultant, National Institute for Smart Government. Author is with Department of Information Technology, Block- 24, Kasumpti, Shimla-171009 HP India (adarsh_khare2@yahoo.com). levels that can be recorded for a given pixel is called radiometric resolution. A radiometric resolution of 8 bit means pixel value ranges from 0-255 (0 – (2 8 -1)). Here, sample image used in this research paper has radiometric resolution of 8 bit. So, scatter plot uses DN values ranging from 0 to 255. The classification is generally carried out on the basis of features like pixels density, texture, etc. in the image. In this approach, the classification is carried out on the basis of pixels’ density in N-dimensional scatter plot. This approach first finds out the gravity centers of pixels on the basis of pixels density in scatter plot. Later it classifies the image using minimum Euclidian distance to gravity centers classifier. Following is the process flow diagram for finding gravity centers in N-dimensional scatter plot and classifying the multispectral image using minimum distance to gravity center classifier. Fig. 1. Process flow diagram of unsupervised classification using N- dimensional scatter plot. In clustering technique like Adaptive Resonance Theory-2, pixels are grouped based upon their minimum distance decision rule and also mean of the cluster is adjusted accordingly. But in the following approach, clusters in N- Unsupervised Classification Using Gravity Centers from Scatter Plot Adarsh Kumar Khare T 2016 Second International Conference on Computational Intelligence & Communication Technology 978-1-5090-0210-8/16 $31.00 © 2016 IEEE DOI 10.1109/CICT.2016.36 140 2016 Second International Conference on Computational Intelligence & Communication Technology 978-1-5090-0210-8/16 $31.00 © 2016 IEEE DOI 10.1109/CICT.2016.36 140