侧扫声呐图像分割的中性集合与量子粒子群算法

doi:10.11947/j.AGCS.2016.20150555

测绘学报 ›› 2016, Vol. 45 ›› Issue (8): 935-942.doi: 10.11947/j.AGCS.2016.20150555

侧扫声呐图像分割的中性集合与量子粒子群算法

赵建虎¹, 王晓¹, 张红梅², 胡俊¹, 简晓敏¹

1. 武汉大学测绘学院, 湖北武汉430079;
2. 武汉大学动力与机械学院, 湖北武汉430072

收稿日期:2015-11-03 修回日期:2016-06-03 出版日期:2016-08-20 发布日期:2016-08-31
通讯作者: 王晓,E-mail:wxsdau2005@163.com E-mail:wxsdau2005@163.com
作者简介:赵建虎(1970-),男,博士,教授,研究方向为海洋测绘.E-mail:jhzhao@sgg.whu.edu.cn
基金资助:
国家自然科学基金（41576107；41376109；41176068）

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

ZHAO Jianhu¹, WANG Xiao¹, ZHANG Hongmei², HU Jun¹, JIAN Xiaomin¹

1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
2. School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China

Received:2015-11-03 Revised:2016-06-03 Online:2016-08-20 Published:2016-08-31
Supported by:
The National Natural Science Foundation of China (Nos. 41576107;41376109;41176068)

摘要/Abstract

摘要： 针对现有的侧扫声呐图像分割方法存在分割准确率不高和效率偏低的问题，提出了一种基于中性集合和量子粒子群算法的侧扫声呐图像阈值分割方法。通过基于中性集合计算图像灰度共生矩阵，实现了侧扫声呐图像精细纹理的表达，提高了分割精度；基于二维最大熵理论，采用量子粒子群算法计算二维最优分割阈值向量，实现了分割阈值向量的快速准确获取，提高了分割效率和精度。最终实现了高噪声侧扫声呐图像目标的准确、高效分割。通过对含有不同目标的侧扫声呐图像的分割试验，验证了该算法的有效性。

关键词: 侧扫声呐图像, 中性集合, 量子粒子群算法, 图像分割

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

中图分类号:

P229

赵建虎, 王晓, 张红梅, 胡俊, 简晓敏. 侧扫声呐图像分割的中性集合与量子粒子群算法[J]. 测绘学报, 2016, 45(8): 935-942.

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.

参考文献

[1] NAKAMURA K,TOKI T,MOCHIZUKI N,et al.Discovery of a New Hydrothermal Vent Based on an Underwater,High-resolution Geophysical Survey[J].Deep Sea Research Part I:Oceanographic Research Papers,2013,74:1-10.
[2] FLOWERS H J,HIGHTOWER J E.A Novel Approach to Surveying Sturgeon Using Side-scan Sonar and Occupancy Modeling[J].Marine and Coastal Fisheries,2013,5(1):211-223.
[3] HEALY C A,SCHULTZ J J,PARKER K,et al.Detecting Submerged Bodies:Controlled Research Using Side-scan Sonar to Detect Submerged Proxy Cadavers[J].Journal of Forensic Sciences,2015,60(3):743-752.
[4] 刘光宇,卞红雨,沈郑燕,等.基于声呐图像的水平集分割算法研究[J].传感器与微系统,2012,31(1):29-31.LIU Guangyu,BIAN Hongyu,SHEN Zhengyan,et al.Research on Level Set Segmentation Algorithm for Sonar Image[J].Transducer and Microsystem Technologies,2012,31(1):29-31.
[5] KÖHNTOPP D,LEHMANN B,KRAUS D,et al.Segmentation and Classification Using Active Contours Based Superellipse Fitting on Side Scan Sonar Images for Marine Demining[C]//Proceedings of 2015 IEEE International Conference on Robotics and Automation (ICRA).Seattle,WA:IEEE,2015:3380-3387.
[6] 王雷,叶秀芬,王天.模糊聚类的侧扫声呐图像分割算法[J].华中科技大学学报(自然科学版),2012,40(9):25-29.WANG Lei,YE Xiufen,WANG Tian.Segmentation Algorithm of Fuzzy Clustering on Side Scan Sonar Image[J].Journal of Huazhong University of Science and Technology (Natural Science Edition),2012,40(9):25-29.
[7] GONG Maoguo,SU Linzhi,JIA Meng,et al.Fuzzy Clustering with a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images[J].IEEE Transactions on Fuzzy Systems,2014,22(1):98-109.
[8] SHAHRIARI H,RANJBAR H,HONARMAND M.Image Segmentation for Hydrothermal Alteration Mapping Using PCA and Concentration:Area Fractal Model[J].Natural Resources Research,2013,22(3):191-206.
[9] 叶秀芬,王兴梅,张哲会,等.改进MRF参数模型的声呐图像分割方法[J].哈尔滨工程大学学报,2009,30(7):768-774.YE Xiufen,WANG Xingmei,ZHANG Zhehui,et al.Sonar Imagery Segmentation Algorithm by Improved MRF Parameter Model[J].Journal of Harbin Engineering University,2009,30(7):768-774.
[10] SONG Sanming,SI Bailu,FENG Xisheng,et al.Prior Parameter Estimation for Ising-MRF-based Sonar Image Segmentation by Local Center-encoding[C]//OCEANS 2015 MTS/IEEE Genova.Genova:IEEE,2015:1-5.
[11] 巫兆聪,胡忠文,张谦,等.结合光谱、纹理与形状结构信息的遥感影像分割方法[J].测绘学报,2013,42(1):44-50.WU Zhaocong,HU Zhongwen,ZHANG Qian,et al.On Combining Spectral,Textural and Shape Features for Remote Sensing Image Segmentation[J].Acta Geodaetica et Cartographica Sinica,2013,42(1):44-50.
[12] VALA H J,BAXI A.A Review on Otsu Image Segmentation Algorithm[J].International Journal of Advanced Research in Computer Engineering&Technology (IJARCET),2013,2(2):387-389.
[13] SUN Jun,FENG Bin,XU Wenbo.Particle Swarm Optimization with Particles Having Quantum Behavior[C]//Proceedings of Congress on Evolutionary Computation.Portland,OR:IEEE,2004,1:325-331.
[14] HANBAY K,TALU M F.Segmentation of SAR Images Using Improved Artificial Bee Colony Algorithm and Neutrosophic Set[J].Applied Soft Computing,2014,21:433-443.
[15] SENGUR A,GUO Yanhui.Color Texture Image Segmentation Based on Neutrosophic Set and Wavelet Transformation[J].Computer Vision and Image Understanding,2011,115(8):1134-1144.
[16] 于博,牛铮,王力,等.一种基于中性集和均值漂移的彩色遥感图像非监督建筑物提取方法[J].光谱学与光谱分析,2013,33(4):1071-1075.YU Bo,NIU Zheng,WANG Li,et al.An Unsupervised Method of Extracting Constructions from Color Remote Sensed Image Based on Mean Shift and Neutrosophic Set[J].Spectroscopy and Spectral Analysis,2013,33(4):1071-1075.
[17] 童小念,施博,王江晴.基于量子粒子群算法的双阈值图像分割方法[J].四川大学学报(工程科学版),2010,42(3):132-138.TONG Xiaonian,SHI Bo,WANG Jiangqing.Dual-threshold Image Segmentation Method Based on QPSO Algorithm[J].Journal of Sichuan University (Engineering Science Edition),2010,42(3):132-138.
[18] OSUNA-ENCISO V,CUEVAS E,SOSSA H.A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation[J].Expert Systems with Applications,2013,40(4):1213-1219.
[19] BLONDEL P.The Handbook of Sidescan Sonar[M].Berlin:Springer Science&Business Media,2009.
[20] REGNIERS O, BOMBRUN L, GUYON D, et al. Wavelet-Based Texture Features for the Classification of Age Classes in a Maritime Pine Forest[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(3):621-625.
[21] POWERS D M. Evaluation:From Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation[J]. Journalof Machine Learning Technologies, 2011, 2(1):37-63.

侧扫声呐图像分割的中性集合与量子粒子群算法

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

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