| 光谱和形状特征相结合的高分辨率遥感图像的建筑物提取方法 |
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| 引用本文： | 吴炜,骆剑承,沈占锋,朱志文.光谱和形状特征相结合的高分辨率遥感图像的建筑物提取方法[J].武汉大学学报(信息科学版),2012,37(7):800-805. |
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| 作者姓名： | 吴炜 骆剑承 沈占锋 朱志文 |
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| 作者单位： | 中国科学院遥感应用研究所,北京市朝阳区大屯路甲3号,100101 |
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| 基金项目： | 国家自然科学基金资助项目，国家科技支撑计划资助项目，中国科学院西部博士资助项目 |
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| 摘 要： | 针对高分辨率遥感影像具有较为丰富的地物属性"谱相"信息和空间分布及其组合"图式"信息的特点,提出了一种光谱和形状特征相结合的建筑物自动提取方法。在多尺度分割和矢量化基础上,根据建筑物的形状、光谱特征,从特征基元中自动选取样本,并计算其特征;通过根据建筑物形状、光谱、纹理构造的模板,在整景影像上进行建筑区域识别,并在建筑区域内提取建筑物外部轮廓。实验表明,本算法具有较高的识别率和较低的误识别率。
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| 关 键 词： | 光谱-形状 建筑物提取 多尺度分割 边缘检测 |
Building Extraction from High Resolution Remote Sensing Imagery Based on Spatial-Spectral Method |
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| WU Wei,LUO Jiancheng,SHEN Zhanfeng,ZHU Zhiwen.Building Extraction from High Resolution Remote Sensing Imagery Based on Spatial-Spectral Method[J].Geomatics and Information Science of Wuhan University,2012,37(7):800-805. |
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| Authors: | WU Wei LUO Jiancheng SHEN Zhanfeng ZHU Zhiwen |
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| Institution: | 1(1 Institute of Remote Sensing Applications,Chinese Academy of Sciences,A 3 Datun Road,Beijing 100101,China) |
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| Abstract: | As is known to all,high resolution remote sensing imagery contains plenty of spectral and spatial information which represent objects’ inner physical attributes and outer distribution and composite structure respectively.According to its characteristics mentioned above,we proposed an building extraction method based on spatial-spectral information on high resolution remote sensing imagery.On the basis of feature unit extraction through multi-scale segmentation and vectorization,we selected building samples automatically from feature units according to characteristics like shape and spectral information.Then,we preceded the building area recognition on the whole image using the template constructed by buildings’ shape,and done edge detection and thinning inside the building regions to extract the outline of building.Last,we implemented the algorithm mentioned above,and proved its validity through experiments. |
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| Keywords: | spatial-spectral coupled building extraction multi scale segmentation edge detection |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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