[1]龙海侠,吴淑雷,吕雁.基于多样性变异的QPSO算法的遥感图像分类[J].智能系统学报编辑部,2015,10(6):938-942.[doi:10.11992/tis.201507045]
LONG Haixia,WU Shulei,LYU Yan.Classification of multispectral remote sensing image based on QPSO and diversity-mutation[J].CAAI Transactions on Intelligent Systems,2015,10(6):938-942.[doi:10.11992/tis.201507045]
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LONG Haixia,WU Shulei,LYU Yan.Classification of multispectral remote sensing image based on QPSO and diversity-mutation[J].CAAI Transactions on Intelligent Systems,2015,10(6):938-942.[doi:10.11992/tis.201507045]
基于多样性变异的QPSO算法的遥感图像分类
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
10
期数:
2015年第6期
页码:
938-942
栏目:
学术论文—机器学习
出版日期:
2015-12-25
- Title:
- Classification of multispectral remote sensing image based on QPSO and diversity-mutation
- Keywords:
- remote sensing image; un-supervised classification; cluster centers; quantum-behaved particle swarm optimization algorithm; diversity-mutation
- 分类号:
- TP391.9
- 摘要:
- 遥感图像分类是遥感领域研究的热点问题之一。结合量子粒子群优化(QPSO)算法和多样性变异的机制提出了一种新的高光谱遥感图像分类算法。在遥感图像分类过程中,采用无监督分类,图像中每个像素点到聚类中心的高斯距离作为分类标准,使用QPSO算法进行聚类中心的优化,在聚类过程中使用多样性变异机制防止QPSO算法早熟收敛,使分类结果达到最优化。在遥感图像上所做的实验表明:此分类算法具有较好的搜索速度和收敛精度,能有效寻找和优化最佳聚类中心,是一种有效、可行的遥感图像分类方法。
- Abstract:
- The classification of remote sensing images is one of the most important issues in remote sensing today. This paper presents a novel classification algorithm for multispectral remote sensing images based on the quantum-behaved particle swarm optimization(QPSO) algorithm and diversity-mutation. To classify remote sensing images, we adopted unsupervised classification, and used the Gaussian distance function between the image pixels and the cluster centers as the classification standard. We used the QPSO algorithm to optimize the cluster centers. For clustering, we propose diversity-mutation to prevent premature convergence of the QPSO algorithm to optimize the classification results. The experimental results show that the proposed algorithm not only has better search speed, but also has higher convergence precision, and searches and optimizes the best cluster center more efficiently. Therefore, we conclude that the algorithm is effective and feasible.
- 参考文献/References:
-
[1] 蒋卫国, 陈强, 郭骥, 等. 基于HPSO和FCM的多光谱遥感图像湿地分类[J]. 光谱学与光谱分析, 2010, 30(12):3329-3333. JIANG Weiguo, CHEN Qiang, GUO Ji, et al. Classification of wetlands in multispectral remote sensing image based on HPSO and FCM[J]. Spectroscopy and Spectral Analysis, 2010, 30(12):3329-3333.
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[3] 董杰, 沈国杰. 一种基于模糊关联分类的遥感图像分类方法[J]. 计算机研究与发展, 2012, 49(7):1500-1506. DONG Jie, SHEN Guojie. Remote sensing image classification based on fuzzy associative classification[J]. Journal of Computer Research and Development, 2012, 49(7):1500-1506.
[4] 袁永福. 基于粒子群和互信息的高光谱图像波段选择和分类[D]. 西安:西安电子科技大学, 2014:61-73. YUAN Yongfu. Band selection and classification for hyperspectral image based on particle swarm optimization and mutual information[D]. Xi’an:Xidian University, 2014:61-73.
[5] UIA D, MUNOZ-MARI J, CAMPS-VALLS G. Remote sensing image segmentation by active queries[J]. Pattern Recognition, 2012, 45(6):2180-2192.
[6] AGRAWAL R K, BAWANE N G. Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery[J]. Applied Soft Computing, 2015, 28:217-225.
[7] SUN Jun, FENG Bin, XU Wenbo. Particle swarm optimization with particles having quantum behavior[C]//Congress on Evolutionary Computation. Portland:IEEE, 2004:325-331.
[8] COELHO L S. Novel gaussian quantum-behaved particle swarm optimiser applied to electromagnetic design[J]. IET Science, Measurement & Technology, 2007, 1(5):290-294.
[9] SUN Jun, CHEN Wei, FANG Wei, et al. Gene expression data analysis with the clustering method based on an improved quantum-behaved Particle Swarm Optimization[J]. Engineering Applications of ArtificialIntelligence, 2012, 25(2):376-391.
[10] SUN Jun, WU Xiaojun, PALADE V, et al. Convergence analysis and improvements of quantum-behaved particle swarm optimization[J]. Information Sciences, 2012, 193:81-103.
[11] URSEM R K. Diversity-guided Evolutionary Algorithm[M]//GUERVóS J J M, ADAMIDIS P, BEYER H G, et al. Parallel Problem Solving from Nature-PPSN VII. Berlin Heidelberg:Springer, 2002, 2439:462-471.
[12] RIGET J, VESTERSTROEM J S. A Diversity-guided particle swarm optimizer-the ARPSO[R]. Aarhus:Department of Computer Science, University of Aarhus, 2002.
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备注/Memo
收稿日期:2015-07-23;改回日期:。
基金项目:国家自然科学基金资助项目(61163042);海南师范大学地理学重点学科基金资助项目(00203030905#).
作者简介:龙海侠,女,1980年生,副教授,博士,主要研究方向为人工智能。主持完成海南省自然科学基金2项,主持完成海南省高等科学研究项目1项。作为第一完成人获得海南省科技进步奖三等奖1项。发表学术论文20余篇。出版专著1部,教材1部。吴淑雷,女,1974年生,教授,主要研究方向为视频、图像和地理信息等。主持和完成国家自然科学基金、海南省自然科学基金多项。发表学术论文30余篇。出版专著2部,教材1部。
通讯作者:吴淑雷.E-mail:595615374@qq.com.
更新日期/Last Update:
1900-01-01