计算机科学 ›› 2017, Vol. 44 ›› Issue (9): 23-27.doi: 10.11896/j.issn.1002-137X.2017.09.004
• CRSSC-CWI-CGrC 2016 • 上一篇 下一篇
刘旋,王国胤,罗小波
LIU Xuan, WANG Guo-yin and LUO Xiao-bo
摘要： 遥感图像技术的迅猛发展,使得传统聚类方法的局限性日益凸显。针对其信息量大、结构复杂等特点,从多粒度、多层次的角度来分析与理解地学现象,能够更好地解决遥感图像的自适应聚类问题。基于云模型与混合高斯相结合的高斯云变换是一种求解多粒度问题的新方法,能够解决问题域中多粒度的生成问题,但是其时间复杂度较高以及对噪声敏感等缺点,导致对遥感图像的聚类结果不理想。因此提出一种改进的高斯云变换方法,首先通过K-Means聚类优化初始粒度的选择,其次结合幅度云综合对粒度跃升策略进行改进,然后使用一种隶属度距离进行粒度的区域划分,最终对遥感图像进行聚类。实验结果验证了所提方法的正确性和有效性。
| [1] BLASCHKE T.Object based image analysis for remote sensing[J].Isprs Journal of Photogrammetry and Remote Sensing,2010,65:2-16. [2] GONG P.Some essential questions in remote sensing science and technology[J].Journal of Remote Sensing,2009,3(1):13-22.(in Chinese) 宫鹏.遥感科学与技术中的一些前沿问题[J].遥感学报,2009,13(1):13-22. [3] LI D Y,DU Y.Artificial Intelligence with Uncertainty[M].Beijing:National Defense Industry Press,2014.(in Chinese) 李德毅,杜鷁.不确定性人工智能[M].北京:国防工业出版社,2014. [4] MA H Y,WANG G Y,ZHANG Q H,et al.Multi-granularity Color Image Segmentation Based on Cloud Model[J].Computer Engineering,2012,8(20):184-187.(in Chinese) 马鸿耀,王国胤,张清华,等.基于云模型的多粒度彩色图像分割[J].计算机工程,2012,8(20):184-187. [5] WANG G Y,XU C L,LI D Y.Generic normal cloud model[J].Information Sciences,2014,280(280):1-15. [6] WANG Z C,LI Y S,XUE L X,et al.Representation of Fuzzy Objects in the Remote Sensing Image[J].Journal of Image & Graphics,2007,2(11):2081-2085.(in Chinese) 王佐成,李永树,薛丽霞,等.遥感图像中模糊对象表达[J].中国图象图形学报,2007,12(11):2081-2085. [7] WU Z C,QIN M Y,ZHANG X.A Cloud-Model-Based Remote Sensing Image Segmentation Concerned with Geometrical Features[J].Geomatics & Information Science of Wuhan University,2008,3(9):939-942.(in Chinese) 巫兆聪,覃茂运,张潇.一种顾及几何特征的云模型遥感图像分割方法[J].武汉大学学报(信息科学版),2008,33(9):939-942. [8] WU Z C,YI L ,ZHANG G F.Uncertainty analysis of object location in multi-source remote sensing imagery classification[J].International Journal of Remote Sensing,2009,30:5473-5487. [9] LIU Y C.Research on particle computing based on cloud model[D].Beijing:Tsinghua University,2013.(in Chinese) 刘玉超.基于云模型的粒计算方法研究[D].北京:清华大学,2013. [10] QIN K,WANG P.New method of curve fitting based on cloud transform[J].Computer Engineering and Applications,2008,44(23):56-74 [11] WANG P B,CAI Z M,LIU W S.EM estimation of PDF para-meters for Gaussian mixture processes[J].Technical Acoustics,2007,6(3):298-502.(in Chinese) 王平波,蔡志明,刘旺锁.混合高斯概率密度模型参数的期望最大化估计[J].声学技术,2007,26(3):298-502. [12] LIU Y C,LI D Y,HE W,et al.Granular computing based on Gaussian cloud transformation[J].Fundamenta Informaticae,2013,127:385-398. [13] MACQUEEN J B.Some methods for classification and analysis of multivariate observations[C]∥ Proc.of Berkeley Symposium on Mathematical Statistics and Probability.1967:281-297. [14] MENG H,WANG S L,LI D Y.Concept extraction and concept hierarchy construction based on cloud transformation[J].Journal of Jilin University,2010,0(3):782-787.(in Chinese) 孟晖,王树良,李德毅.基于云变换的概念提取及概念层次构建方法[J].吉林大学学报,2010,40(3):782-787. [15] MAJI P,PAL S K.Rough set based generalized fuzzy C-means algorithm and quantitative indices[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2007,37(6):1529-1540. [16] COMANICIU D.Mean shift:A robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619. [17] YI L,LIU P,QIAO X,et al.Multi-granularity synthesis segmentation for high spatial resolution Remote sensing images[J].Iop Conference Series Earth & Environmental Science,2014,17(1):682-691. |
| No related articles found! |
|
||
首页