Babawuro Usman/ Elixir Comp. Sci. & Engg. 63 (2013) 18671-18675 18671 Introduction Satellite imagery is an indispensable tool in scientific research and environmental planning, with applications in numerous fields. One of the applications is in Image classification. Image classification approaches have evolved over the years. This development has been driven by the need for higher accuracies in the classified results coupled with the emergence of high resolution satellite imageries, such as Quick Bird and IKONOS which pose a greater challenge to many image classification methods [1]. The purpose of image classification is to label the pixels in the image with meaningful information of the real world for better and useful information extraction. Through classification of satellite imagery, thematic maps bearing the information such as cadastral information, land cover type, vegetation type, etc. could be obtained [2]. Image, classification methods may be grouped into two main categories depending on the image primitive used, viz pixel based or object based methods. Pixel based methods classify individual pixels without taking into account any spatial information of the pixel. Only the spectral patterns are used. On the other hand, object based methods attempt to group pixels into objects by an image segmentation process based on a chosen similarity, e.g., texture, color, intensity and then use the spectral, spatial and contextual information inherent in these objects to classify the whole image [1]. It has emerged as a superior way of doing image classification. One of its strength is the ability to extract real world objects, proper in shape and accurate in classification. It eliminates the mixed pixel problem suffered by most pixel based methods. This is because the image is classified on an object level and usually more information is used. Object based methods are also able to handle high resolution satellite imagery which aggravates the classification process for most pixel based methods [1]. Classification techniques include conventional statistical algorithms, such as discriminate analysis and the maximum likelihood classification, which allocate each image pixel to the land cover class in which it has the highest probability of membership [4]. One of the major disadvantages of these classifiers is that they are not distribution free [5]. In the same vain, traditional pixel-based classification methods have difficulty with high resolution satellite imagery, resulting in a “salt and pepper” appearance. In this paper, an attempt has been made to practical classify high resolution satellite imagery into accurate spatial groups with the following classes using the object based approach for cadastral, environmental studies and management. The classes are farmlands, bare lands, built-up areas, and others. In this study, the classification of the imagery has been done using color k-means clustering algorithm, where the imagery was classified into various classes with a view to determine the most optimum clusters based on apriori knowledge of the imagery, and then the land cover classification was performed. The aim has been to identify and classify farmlands for statutory environmental functions. Initially the imagery has been georectified to assume the planer surface that could be needed for environmental quantitative image analysis [5]. The paper is organized as follows: In section 2, we describe the related work of the paper while in Section 3, we describe the color segmentation scheme and the implementation process. In Section 4, we give experimental results and an evaluation method was presented in Section 5. Finally in Section 6, we draw a concise conclusion. Related Work Lonesome M. M. [6], used a region-based approach for doing image classification. The main goal was to develop an alternative procedure for an object-based image classification. The procedure significantly reduced the mixed pixel problem suffered by most pixel based methods. Wen C. et al. [7], presented a Satellite image classification method using color and Satellite Imagery Land Cover Classification using K-Means Clustering Algorithm Computer Vision for Environmental Information Extraction Babawuro Usman Department of Computer Science, Faculty of Computing and Mathematical Sciences, Kano University of Science and Technology, Wudil, Kano State, Nigeria. ABSTRACT Segmentation and classification of high resolution satellite imagery is a challenging problem due to the fact that it is no longer meaningful to carry out this task on a pixel-by-pixel basis. The fine spatial resolution implies that each object is an aggregation of a number of pixels in close spatial proximity, and accurate classification requires that this aspect be subtly considered. K-means clustering algorithm is a better method of classifying high resolution satellite imagery. The extracted regions are classified using a minimum distance decision rule. Several regions are selected as training samples for region classification. Each region is compared to the training samples and is assigned to its closest class. The procedure significantly reduces the mixed pixel problem suffered by most pixel based methods. In this paper, we used K-means clustering algorithm to classify satellite imagery into specific objects within it for cadastral and environmental planning purposes, thereby eliminating the above mentioned problems and getting better classification accuracy with the overall performance for accuracy percentage as 88.889% and Kappa values as 0.835. © 2013 Elixir All rights reserved ARTICLE INFO Article history: Received: 10 August 2013; Received in revised form: 2 October 2013; Accepted: 21 October 2013; Keywords K-means algorithm, Clustering, Satellite Imagery, Classification. Elixir Comp. Sci. & Engg. 63 (2013) 18671-18675 Computer Science and Engineering Available online at www.elixirpublishers.com (Elixir International Journal) Tele: E-mail addresses: wuroabu@yahoo.com © 2013 Elixir All rights reserved