SAR image segmentation based on Artificial Bee Colony algorithm

  • Authors:

  • Miao Ma;Jianhui Liang;Min Guo;Yi Fan;Yilong Yin

  • Affiliations:

  • School of Computer Science, Shaanxi Normal University, Xi'an 710062, PR China and School of Computer Science and Technology, Shandong University, Jinan 250101, PR China;School of Computer Science, Shaanxi Normal University, Xi'an 710062, PR China;School of Computer Science, Shaanxi Normal University, Xi'an 710062, PR China;School of Computer Science, Shaanxi Normal University, Xi'an 710062, PR China;School of Computer Science and Technology, Shandong University, Jinan 250101, PR China

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2011

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Abstract

Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR) images is still a challenging problem. This paper proposes a fast SAR image segmentation method based on Artificial Bee Colony (ABC) algorithm. In this method, threshold estimation is regarded as a search procedure that searches for an appropriate value in a continuous grayscale interval. Hence, ABC algorithm is introduced to search for the optimal threshold. In order to get an efficient fitness function for ABC algorithm, after the definition of grey number in Grey theory, the original image is decomposed by discrete wavelet transform. Then, a filtered image is produced by performing a noise reduction to the approximation image reconstructed with low-frequency coefficients. At the same time, a gradient image is reconstructed with some high-frequency coefficients. A co-occurrence matrix based on the filtered image and the gradient image is therefore constructed, and an improved two-dimensional grey entropy is defined to serve as the fitness function of ABC algorithm. Finally, by the swarm intelligence of employed bees, onlookers and scouts in honey bee colony, the optimal threshold is rapidly discovered. Experimental results indicate that the proposed method is superior to Genetic Algorithm (GA) based and Artificial Fish Swarm (AFS) based segmentation methods in terms of segmentation accuracy and segmentation time.