[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]
点击复制
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.
[2] SHANKAR B U, MEHER S K, GHOSH A. Wavelet-fuzzy hybridization:feature-extraction and land-cover classification of remote sensing images[J]. Applied Soft Computing, 2011, 11(3):2999-3011.
[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.
- 相似文献/References:
-
[1]刘富,于鹏,刘坤.采用独立分量分析Zernike矩的遥感图像飞机目标识别[J].智能系统学报编辑部,2011,6(1):51.
LIU Fu,YU Peng,LIU Kun.Research concerning aircraft recognition of remote sensing images based on ICA Zernike invariant moments[J].CAAI Transactions on Intelligent Systems,2011,6():51.
[2]吴诗婳,吴一全,周建江.直线截距直方图城区遥感图像多阈值分割[J].智能系统学报编辑部,2018,13(2):227.[doi:10.11992/tis.201609012]
WU Shihua,WU Yiquan,ZHOU Jianjiang.Multi-level thresholding for remote sensing image of urban area based on line intercept histogram[J].CAAI Transactions on Intelligent Systems,2018,13():227.[doi:10.11992/tis.201609012]
[3]李亚飞,董红斌.基于卷积神经网络的遥感图像分类研究[J].智能系统学报编辑部,2018,13(4):550.[doi:10.11992/tis.201706078]
LI Yafei,DONG Hongbin.Classification of remote-sensing image based on convolutional neural network[J].CAAI Transactions on Intelligent Systems,2018,13():550.[doi:10.11992/tis.201706078]
[4]王昌安,田金文.生成对抗网络辅助学习的舰船目标精细识别[J].智能系统学报编辑部,2020,15(2):296.[doi:10.11992/tis.201901004]
WANG Changan,TIAN Jinwen.Fine-grained inshore ship recognition assisted by deep-learning generative adversarial networks[J].CAAI Transactions on Intelligent Systems,2020,15():296.[doi:10.11992/tis.201901004]
[5]王晓林,苏松志,刘晓颖,等.一种基于级联神经网络的飞机检测方法[J].智能系统学报编辑部,2020,15(4):697.[doi:10.11992/tis.201908028]
WANG Xiaolin,SU Songzhi,LIU Xiaoying,et al.Cascade convolutional neural networks for airplane detection[J].CAAI Transactions on Intelligent Systems,2020,15():697.[doi:10.11992/tis.201908028]
[6]赵文清,康怿瑾,赵振兵,等.改进YOLOv5s的遥感图像目标检测[J].智能系统学报编辑部,2023,18(1):86.[doi:10.11992/tis.202203013]
ZHAO Wenqing,KANG Yijin,ZHAO Zhenbing,et al.A remote sensing image object detection algorithm with improved YOLOv5s[J].CAAI Transactions on Intelligent Systems,2023,18():86.[doi:10.11992/tis.202203013]
[7]刘庆鑫,李霓,贾鹤鸣,等.改进䲟鱼优化算法和熵测度的图像多阈值分割[J].智能系统学报编辑部,2024,19(2):381.[doi:10.11992/tis.202205018]
LIU Qingxin,LI Ni,JIA Heming,et al.An improved remora optimization algorithm for multilevel thresholding image segmentation using an entropy measure[J].CAAI Transactions on Intelligent Systems,2024,19():381.[doi:10.11992/tis.202205018]
[8]邵凯,王明政,王光宇.基于Transformer的多尺度遥感语义分割网络[J].智能系统学报编辑部,2024,19(4):920.[doi:10.11992/tis.202304026]
SHAO Kai,WANG Mingzheng,WANG Guangyu.Transformer-based multiscale remote sensing semantic segmentation network[J].CAAI Transactions on Intelligent Systems,2024,19():920.[doi:10.11992/tis.202304026]
[9]梁礼明,冯耀,龙鹏威,等.基于MobileViT和多尺度特征聚合的遥感图像目标检测[J].智能系统学报编辑部,2024,19(5):1168.[doi:10.11992/tis.202310022]
LIANG Liming,FENG Yao,LONG Pengwei,et al.Remote sensing image object detection based on MobileViT and multiscale feature aggregation[J].CAAI Transactions on Intelligent Systems,2024,19():1168.[doi:10.11992/tis.202310022]
[10]曲海成,李瑞柯,王蒙,等.基于特征重用和膨胀卷积的遥感图像舰船检测[J].智能系统学报编辑部,2024,19(5):1298.[doi:10.11992/tis.202304002]
QU Haicheng,LI Ruike,WANG Meng,et al.Ship detection in remote sensing images via feature reuse and dilated convolution[J].CAAI Transactions on Intelligent Systems,2024,19():1298.[doi:10.11992/tis.202304002]
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