k均值聚类引导的遥感影像多尺度分割优化方法

doi:10.11947/j.AGCS.2015.20130497

测绘学报 ›› 2015, Vol. 44 ›› Issue (5): 526-532.doi: 10.11947/j.AGCS.2015.20130497

k均值聚类引导的遥感影像多尺度分割优化方法

王慧贤^1,2, 靳惠佳¹, 王娇龙³, 江万寿¹

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

收稿日期:2013-12-26 修回日期:2014-11-04 出版日期:2015-05-20 发布日期:2015-05-27
通讯作者: 江万寿 E-mail: jws@whu.edu.cn E-mail:jws@whu.edu.cn
作者简介: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
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)

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

WANG Huixian^1,2, JIN Huijia¹, WANG Jiaolong³, JIANG Wanshou¹

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:2013-12-26 Revised:2014-11-04 Online:2015-05-20 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)

摘要/Abstract

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

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

中图分类号:

P237

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.

WANG Huixian, JIN Huijia, WANG Jiaolong, JIANG Wanshou. Optimization Approach for Multi-scale Segmentation of Remotely Sensed Imagery under k-means Clustering Guidance[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(5): 526-532.

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k均值聚类引导的遥感影像多尺度分割优化方法

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

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