The Neutrosophic Set and Quantum-behaved Particle Swarm Optimization Algorithm of Side Scan Sonar Image Segmentation

doi:10.11947/j.AGCS.2016.20150555

Abstract

Abstract: Due to the problem of the existing image segmentation methods applied in side scan sonar (SSS) image often suffered from low efficiency or low accuracy, this paper proposed a novel SSS image thresholding segmentation method based on neutrosophic set (NS) and quantum-behaved particle swarm optimization (QPSO) algorithm. Firstly, the image gray co-occurrence matrix is constructed in NS domain, the fine texture of SSS image is expressed, and this can improve the accuracy of SSS image segmentation. Then, based on the two-dimensional maximum entropy theory, the optimal two-dimensional segmentation threshold vector is quickly and accurately obtained by QPSO algorithm, and this can improve the efficiency and accuracy of SSS image segmentation. Finally, the accurate and high efficient target segmentation of SSS image with high noises is realized. The effectiveness of the algorithm is verified by segmenting SSS image containing different targets.

Key words: side scan sonar (SSS) image, neutrosophic set (NS), quantum-behaved particle swarm optimization (QPSO) algorithm, image segmentation

CLC Number:

P229

ZHAO Jianhu, WANG Xiao, ZHANG Hongmei, HU Jun, JIAN Xiaomin. The Neutrosophic Set and Quantum-behaved Particle Swarm Optimization Algorithm of Side Scan Sonar Image Segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(8): 935-942.

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