Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means

Abstract

Image segmentation is an important task in image analysis and processing. Many of the existing methods for segmenting a multi-component image (satellite or aerial) are very slow and require a priori knowledge of the image that could be difficult to obtain. Furthermore, the success of each of these methods depends on several factors, such as the characteristics of the acquired image, resolution limitations, intensity in-homogeneities and the percentage of imperfections induced by the process of image acquisition. Recently, fuzzy C-means (FCM) and Genetic Algorithms were separately used in segmenting multi-component images but neither of them had successfully addressed the above concerns. GA was enhanced using Hill-climbing, randomising, and modified mutation operators, leading to what is called hybrid dynamic genetic algorithm (HDGA). Coupling HDGA and FCM creates an unsupervised segmentation method which could successfully segment two types of multi-component images (Landsat ETM+, and IKONOS II). Comparison with the four different methods FCM, hybrid genetic algorithm (HGA), self-organizing-maps (SOM), and the combination of SOM and HGA (SOM-HGA) reveals that FCM-HDGA segmentation method gives robust and reliable results, and is more time efficient.