k均值聚类引导的遥感影像多尺度分割优化方法

王慧贤; 靳惠佳; 王娇龙; 江万寿

doi:10.11947/j.AGCS.2015.20130497

测绘学报 >

2015 , Vol. 44 >Issue 5: 526 - 532

DOI: https://doi.org/10.11947/j.AGCS.2015.20130497

摄影测量学与遥感

k均值聚类引导的遥感影像多尺度分割优化方法

王慧贤 ,
靳惠佳 ,
王娇龙 ,
江万寿

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1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 中国科学院电子学研究所空间信息处理与应用系统技术重点实验室, 北京 100190;
3. 河北省制图院, 河北石家庄 050031

王慧贤(1985—),女,助理研究员,研究方向为遥感影像处理与分析。E-mail: hxwang@mail.ie.ac.cn

收稿日期: 2013-12-26

修回日期: 2014-11-04

网络出版日期: 2015-05-27

基金资助

国家973计划(2011CB707105);国家863计划(2013AA12A301);长江学者和创新团队发展计划(IRT1278)

收起

Optimization Approach for Multi-scale Segmentation of Remotely Sensed Imagery under k-means Clustering Guidance

WANG Huixian ,
JIN Huijia ,
WANG Jiaolong ,
JIANG Wanshou

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1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
3. Hebei Provincial Institute of Cartography, Shijiazhuang 050031, China

Received date: 2013-12-26

Revised date: 2014-11-04

Online published: 2015-05-27

Supported by

The National Basic Research Program of China(973 Program)(No. 2011CB707105);The National High-tech Research and Development Program of China (863 Program) (No. 2013AA12A301);Program for Changjiang Scholars and Innovative Research Team in University(No. IRT1278)

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摘要

针对不同尺度地物的分割需求,提出了一种k均值聚类引导的多尺度分割优化方法。首先对原始影像进行小尺度分割和k均值聚类,然后利用k均值聚类结果引导对象合并,在合并过程中利用Otsu阈值方法自动选择k均值聚类的影响因子,最终得到适应不同尺度地物的分割结果。以FNEA多尺度分割方法为例,利用模拟数据和真实的GeoEye-1影像数据进行相关试验,目视和定量评价表明本文方法能够得到适宜不同尺度地物的高质量分割结果。

关键词： 多尺度分割; k均值聚类; 引导优化; FNEA; Otsu阈值法

本文引用格式

王慧贤 , 靳惠佳 , 王娇龙 , 江万寿 . k均值聚类引导的遥感影像多尺度分割优化方法[J]. 测绘学报, 2015 , 44(5) : 526 -532 . DOI: 10.11947/j.AGCS.2015.20130497

Abstract

In order to adapt different scale land cover segmentation, an optimized approach under the guidance of k-means clustering for multi-scale segmentation is proposed. At first, small scale segmentation and k-means clustering are used to process the original images; then the result of k-means clustering is used to guide objects merging procedure, in which Otsu threshold method is used to automatically select the impact factor of k-means clustering; finally we obtain the segmentation results which are applicable to different scale objects. FNEA method is taken for an example and segmentation experiments are done using a simulated image and a real remote sensing image from GeoEye-1 satellite, qualitative and quantitative evaluation demonstrates that the proposed method can obtain high quality segmentation results.

Key words： multi-scale segmentation; k-means clustering; guidance optimization; FNEA; Otsu threshold method

参考文献

[1] VANHAMEL I, PRATIKAKIS I, SAHLI H. Multiscale Gradient Watersheds of Color Images[J]. IEEE Transactions on Image Processing, 2003, 12(6): 617-626.
[2] XIAO Pengfeng, FENG Xuezhi, ZHAO Shuhe, et al. Segmentation of High Resolution Remotely Sensed Imagery Based on Phase Congruency[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(2): 146-151. (肖鹏峰, 冯学智, 赵书河, 等. 基于相位一致的高分辨率遥感图像分割方法[J]. 测绘学报, 2007, 36(2): 146-151.)
[3] COMANICIU D, MEER P. Mean Shift: A Robust Approach toward Feature Space Analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.
[4] NOCK R, NIELSEN F. Statistical Region Merging[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(11): 1452-1458.
[5] HAN Bing, ZHAO Yindi, GE Lele. Wavelet-domain in HMT Model Based on Iterative Context Fusion for Remote Sensing Image Segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(2): 233-237. (韩冰, 赵银娣, 戈乐乐. 遥感图像分割的迭代上下文融合小波域HMT模型[J]. 测绘学报, 2013, 42(2): 233-237.)
[6] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient Graph-based Image Segmentation[J]. International Journal of Computer Vision, 2004, 59(2): 167-181.
[7] BAATZ M, SCHPE A. Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation[C]//Angewandte Geographische Informationsverarbeitung XII: Beitrge zum AGIT-Symposium. Salzburg: Herbert Wichmann Verlag, 2000: 12-23.
[8] IKOKOU G B, SMIT J. A Technique for Optimal Selection of Segmentation Scale Parameters for Object-oriented Classification of Urban Scenes[J]. South African Journal of Geomatics, 2013, 2(4): 358-369.
[9] LI Qin, GAO Xizhang, ZHANG Tao, et al. Optimal Segmentation Scale Selection and Evaluation for Multi-layer Image Recognition and Classification[J]. Journal of Geo-Information Science, 2011, 13(3): 409-417. (李秦, 高锡章, 张涛, 等. 最优分割尺度下的多层次遥感地物分类实验分析[J]. 地球信息科学学报, 2011, 13(3): 409-417.)
[10] KIM M, MADDEN M, WARNER T. Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery[M]//Object-based Image Analysis. Berlin: Springer, 2008: 291-307.
[11] HUANG Huiping. The Scale of the Problem of Object-oriented Image Analysis[D]. Beijing: Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, 2003, 124-126. (黄慧萍. 面向对象影像分析中的尺度问题研究[D]. 北京: 中国科学院遥感应用研究所, 2003, 124-126.)
[12] YU Huan, ZHANG Shuqing, KONG Bo, et al. Selection of the Optimal Segmentation Scale in Object-oriented Remote Sensing Image Classification[J]. Journal of Image and Graphics, 2010, 15(2): 352-360. (于欢, 张树清, 孔博, 等. 面向对象遥感影像分类的最优分割尺度选择研究[J]. 中国图象图形学报, 2010, 15(2): 352-360.)
[13] CHEN Jianyu, PAN Delu, MAO Zhihua. High-resolution Remote Sensing Images Coastal Optimal Partition of Simple Objects-scale Problems[J]. Scientia Sinica Terrae, 2006, 36(11): 1044-1051. (陈建裕, 潘德炉, 毛志华. 高分辨率海岸带遥感影像中简单地物的最优分割尺度问题[J]. 中国科学: 地球科学, 2006, 36(11): 1044-1051.)
[14] WANG Xiaojun, LEESER M. K-means Clustering for Multispectral Images Using Floating-point Divide[C]//Proceedings of the 15th Annual IEEE Symposium on Field-programmable Custom Computing Machines. Napa, CA: IEEE, 2007: 151-162.
[15] OTSU N. A Threshold Selection Method from Gray-level Histograms[J]. IEEE Transactions on Systems, Man and Cybernetics, 1979, 9(1): 62-66.
[16] POLAK M, ZHANG Hong, PI Minghong. An Evaluation Metric for Image Segmentation of Multiple Objects[J]. Image and Vision Computing, 2009, 27(8): 1223-1227.
[17] ZHOU Jiaxiang. Study on Mean Shift Segmentation and Application of Remotely Sensed Imagery[D]. Changsha: Central South University, 2012. (周家香. Mean Shift 遥感图像分割方法与应用研究[D]. 长沙: 中南大学, 2012.)
[18] MEILA M. Comparing Clusterings: An Information Based Distance[J]. Journal of Multivariate Analysis, 2007, 98(5): 873-895.
[19] NEUBERT M, MEINEL G. Evaluation of Segmentation Programs for High Resolution Remote Sensing Applications[C]//Proceedings of the Joint ISPRS/EARSel Workshop:High Resolution Mapping from Space. Hannover, Germany: [s.n.], 2003.
[20] ROTTENSTEINER F, TRINDER J, CLODE S, et al. Using the Dempster-shafer Method for the Fusion of LiDAR Data and Multi-spectral Images for Building Detection[J]. Information Fusion, 2005, 6(4): 283-300.

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