| k均值聚类引导的遥感影像多尺度分割优化方法 |
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| 引用本文： | 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 |
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| 作者姓名： | 王慧贤 靳惠佳 王娇龙 江万寿 |
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| 作者单位： | 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 |
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| 基金项目： | 国家973计划，国家863计划，长江学者和创新团队发展计划(IRT1278)@@@@ The National Basic Resea rch Prog ram of Ch i na (973 Prog ram )，The National H i gh-tech Resea rch and Deve l opment Prog ram of Ch i na (863 Pro-g ram)，Prog ram fo r Changj i ang Scho l a rs and I nnova t ive Resea rch Team i n Un iversi ty |
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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。
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| 关 键 词： | 多尺度分割 k均值聚类 引导优化 FNEA Otsu阈值法 |
| 收稿时间： | 2013-12-26 |
| 修稿时间： | 2014-11-04 |
Optimization Approach for Multi-scale Segmentation of Remotely Sensed Imagery under k-means Clustering Guidance |
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| 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. DOI: 10.11947/j.AGCS.2015.20130497 |
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| Authors: | WANG Huixian JIN Huijia WANG Jiaolong JIANG Wanshou |
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| Affiliation: | 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 |
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| 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 fi rst,smal l 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 automatical ly select the impact factor of k-means clustering;final ly we obtain the segmentation results which are appl icable 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 satel l ite,qual itative and quantitative evaluation demonstrates that the proposed method can obtain high qual ity segmentation results. |
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| Keywords: | mul ti-scale segmentation k-means clustering guidance optimization FNEA Otsu threshold method |
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