Color Image Segmentation for Satallite Images
Year: 2011

This paper introduces efficient and fast algorithms for unsupervised image segmentation, using low level features such as color, applied on satellite images. With the increase in spatial resolution of satellite imagery, the image segmentation technique for generating and updating geographical information are becoming more and more important. The present paper describes a satellite image segmentation technique using M band fuzzy c Means features. In remotely sensed multispectral imagery the variations in the reflectivity of surface materials across different spectral bands provide a fundamental mechanism for understanding the image features. Fuzzy methods in remote sensing have received growing interest for their importance in situations where the geographical phenomena are inherently fuzzy. The proposed approach is based on that first enhance multispectral image and then applying clustering technique, using La*b* color space and the vectors are used as inputs for the k means or fuzzy c means clustering methods, for a segmented image whose regions are distinct from each other according to color and texture characteristics. KeywordsImage segmentation; k means; fuzzy c means (FCM) La*b* color space.  Satellite image segmentation has been a focused research area in the image processing, for the last few decades. Many papers has been published, mainly focused on gray scale images and less attention on color image segmentation, which convey much more information about the object or images. Image segmentation is typically used to locate objects and boundaries in images. Image segmentation is a process of partitioning image pixels based on selected image features. The pixels that belong to the same region must be spatially connected and have the similar image features. If the selected segmentation feature is color, an image segmentation process would separate pixels that have distinct color feature into different regions, and, simultaneously, group pixels that are spatially connected and have the similar color into the same region  In color imagery, image pixels can be represented in a number of different color spaces e.g. RGB, HSV or LUV [5, 9, and 10] The CIELAB color space, adopted as an international standard in the 1970's, provides perceptually uniform space, which means the Euclidean distance between two color points in the CIELAB color space corresponds to the perceptual difference between the two colors by the human vision system  This property has made the CIELAB color space to be attractive and useful for color analysis, and the CIELAB color space has shown its superior performance in many color image applications [2]  Based on these reports, the CIELAB color apace has been chosen for color clustering. High resolution images are useful for applications such as transportation network mapping, disaster preparedness, urban planning, precision farming, and telecommunications. On the other hand, low resolution satellite images, with ground resolution greater than 10m, are useful for applications like environmental assessment, regional mapping, forestry management, widespread disaster assessment, and urban monitoring. It is often necessary to enhance multispectral radiance or reflectance data to create an image that is suitable for visual interpretation. Manimala Singha et al. International Journal on Computer Science and Engineering (IJCSE) ISSN 0975  Clustering is an unsupervised, computationally efficient and simplest technique, which can be applied to multi dimensional data and the results are meaningful only if the homogenous non textured color regions define the image data, in general   The pixel based segmentation technique, consisting of Fuzzy C Means [6] and K Means [14] considers only the spectral pattern to segment the image. These techniques are not sufficient to segment high resolution satellite images due to the variability of spectral and structural information in such images. This paper considers the segmentation problem of image regions based on two clustering methods: color feature similarity using fuzzy c means or k means. The rest of the paper is organized as follows: In section I, a brief description the clustering techniques presented. The section II describes the Image segmentation using kmeans (KM) The section III, Image segmentation using fuzzy Cmeans (FCM) The experimental results are given in section IV. Finally the conclusions are presented in section V. I. CLUSTERING TECHNIQUE ON IMAGES Clustering is considered to be the most important unsupervised learning problem. It deals with finding a structure in a collection of unlabeled data. Clustering is the process of organizing objects into groups whose members are similar in some way. A cluster is therefore a collection of objects which are "similar" between them and are "dissimilar" to the objects belonging to other clusters. The two basic clustering techniques are Kmeans (an exclusive clustering algorithm) and Fuzzy C means (an overlapping clustering algorithm)    A. K Means Algorithm K means algorithm was originally introduced by McQueen in 1967 [22] It is a non fuzzy clustering method whereby each pattern can only belong to one centre at any one time. The K means algorithm is an iterative technique that is used to partition an image into K clusters. Let X {x1, x2. xn} represent a set of pixels of the given image, where n is the number of pixels. V {v1, v2. vk} is the corresponding set of cluster centres, where k is the number of clusters. The aim of K means algorithm is to minimize the objective function J(V) in this case a squared error function: J(V) x v (1) Where, x v is the Euclidean distance between xij and vj.ki is the number of pixels in the cluster i. The difference is typically based on pixel colour, intensity, texture, and location, or a weighted combination of these factors. In our study, we have considered pixel intensity. The i cluster centre vi can be calculated as: (2)