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Satellite Image Segmentation Using Hybrid Variable Genetic Algorithm
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DOI：10.1002/ima.20195.png)
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Abstract
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Image segmentation is an important task in image processing and analysis. Many segmentation methods have been used to segment satellite images. The success of each method depends on the characteristics of the acquired image such as resolution limitations and on the percentage of imperfections in the process of image acquisition due to noise. Many of these methods require a priori knowledge which is difficult to obtain. Some of them are parametric statistical methods that use many parameters which are dependent on image property. In this article, a new unsupervised nonparametric method is developed to segment satellite images into homogeneous regions without any a priori knowledge. The new method is called hybrid variable genetic algorithm (HVGA). The variability is found in the variable number of cluster centers and in the changeable mutation rate. In addition, this new method uses different heuristic processes to increase the efficiency of genetic algorithm in avoiding local optimal solutions. Experiments performed on two different satellite images (Landsat and Spot) proved the high accuracy and efficiency of l compared with another two unsupervised and nonparametric segmentation methods genetic algorithm (GA) and self-organizing map (SOM). The verification of the results included stability and accuracy measurements using an evaluation method implemented from the functional model (FM) and field surveys. (C) 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 199-207, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DO] 10.1002/ima.20195
Keywords:
satellite image
genetic algorithm
segmentation
unsupervised
nonparametric
hill-climbing
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