COLOUR BASED IMAGE SEGMENTATION USING FUZZY C-MEANS CLUSTERING

Abstract

Primarily due to the progresses in spatial resolution of satellite imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more and more important. This work presents a novel image segmentation based on colour features with K-means clustering unsupervised algorithm. In this we did not used any training data. The entire work is divided into two stages. First enhancement of color separation of satellite image using decorrelation stretching is carried out and then the regions are grouped into a set of five classes using K-means clustering algorithm. Using this two step process, it is possible to reduce the computational cost avoiding feature calculation for every pixel in the image. Although the colour is not frequently used for image segmentation, it gives a high discriminative power of regions present in the image. 1. Introduction: In remote sensing, the process of image segmentation is defined as: " the search for homogenous regions in an image and later the classification of these regions " . It also means the partitioning of an image into meaningful regions based on homogeneity or heterogeneity criteria (Haralick et al; 1992). Image segmentation techniques can be differentiated into the following basic concepts: pixel oriented, Contour-oriented, region-oriented, model-oriented, colour oriented and hybrid (M.Neubert,et al; 2005). Colour segmentation of image is a crucial operation in image analysis and in many computer vision, image interpretation, and pattern recognition system, with applications in scientific and industrial field(s) such as medicine, Remote Sensing, Microscopy, content-based image and video retrieval, document analysis, industrial automation and quality control (Ricardo Dutra, et al;2008). The performance of colour segmentation may significantly affect the quality of an image understanding system (H.S.Chen et al; 2006).The most common features used in image segmentation include texture, shape, grey level intensity, and colour.The constitution of the right data space is a common problem in connection with segmentation/classification. In order to construct realistic classifiers, the features that are sufficiently representative of the physical process must be searched. In the literature, it is observed that different transforms are used to extract desired information from remote-sensing images or biomedical images (Mehmet Nadir Kurnaz et al; 2005).Segmentation evaluation techniques can be generally divided into two categories (supervised and unsupervised). The first category is not applicable to remote sensing because an optimum segmentation (ground truth segmentation) is difficult to obtain. Moreover, available segmentation evaluation techniques have not been thoroughly tested for remotely sensed data. Therefore, for comparison purposes, it is possible to proceed with the classification process and then indirectly assess the segmentation process through the produced classification accuracies. (Ahmed Darwish, et al; 2003). For image segment based classification, the images that need to be classified are segmented into many homogeneous areas with similar spectrum information firstly, and the image segments' features are extracted based on the specific requirements of ground features classification. The color homogeneity is based on the standard deviation of the spectral colors, while the shape homogeneity is based on the compactness and