| 多尺度SLIC-GMRF与FCNSVM联合的高分影像建筑物提取 |
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| 引用本文： | 井然,宫兆宁,朱文定,关鸿亮,赵文吉,张涛.多尺度SLIC-GMRF与FCNSVM联合的高分影像建筑物提取[J].遥感学报,2020,24(1):11-26. |
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| 作者姓名： | 井然 宫兆宁 朱文定 关鸿亮 赵文吉 张涛 |
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| 作者单位： | 1.首都师范大学 资源环境与旅游学院，北京 100048;2.三维信息获取与应用教育部重点实验室，北京 100048;3.资源环境与地理信息系统北京市重点实验室，北京 100048;4.国家测绘地理信息局卫星测绘应用中心，北京 100048 |
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| 基金项目： | 国家高分专项高分遥感测绘应用示范系统项目(编号：AH1601-8) |
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| 摘 要： | 遥感影像建筑物提取具有重要的应用价值。然而,高分辨率遥感影像中细节信息繁多、特征复杂,增加了建筑物提取难度。针对这一问题,本文提出一种基于多尺度SLIC-GMRF和FCNSVM的建筑物提取方法,一定程度上提高了高分辨率遥感影像建筑物提取能力。首先,利用多尺度SLIC-GMRF分割算法确定初始建筑物区域,然后,充分利用FCN神经网络在语义分割中的优势抽取建筑物特征,最后,结合提取出的建筑物特征训练SVM分类器细化建筑物提取结果,通过3种控制实验,两种对比方法得出以下结论:SLIC分割算法影响初始分割结果;SVM分类器影响建筑物细部提取;FCN特征影响SVM分类器性能。对于特征清晰、遮挡干扰较少的研究区,本文方法能够较好提取影像中的建筑物,查准率、查全率、质量指标均优于对比方法,对建筑物复杂分布的研究区同样能够取得较好的提取效果。
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| 关 键 词： | 遥感 建筑物提取 影像分割 FCN神经网络 支持向量机 高分辨率遥感影像 |
| 收稿时间： | 2018/5/16 0:00:00 |
Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM |
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| JING Ran,GONG Zhaoning,ZHU Wending,GUAN Hongliang,ZHAO Wenji,ZHANG Tao.Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM[J].Journal of Remote Sensing,2020,24(1):11-26. |
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| Authors: | JING Ran GONG Zhaoning ZHU Wending GUAN Hongliang ZHAO Wenji ZHANG Tao |
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| Abstract: | The extraction of buildings from remote sensing imagery has an important application value. However, high-resolution images contain detailed information and complex features that hinder the difficulty of building extraction process. To address this problem, we propose a building extraction method of building extraction based on multi-scale SLIC-GMRF and FCNSVM that demonstrates an improved ability of extracting buildings from high-resolution remote sensing images to some extent. First, a multi-scale SLIC-GMRF segmentation algorithm is applied to determine the initial building area, and then the advantages of the FCN neural network in semantic segmentation are utilized to extract the building features. Second, the extracted building features are used to train an SVM classifier to refine the building extraction results of building. The results of three control experiments and two comparative tests reveal that the SLIC segmentation algorithm affects the initial segmentation results, the SVM classifier affects the extraction of building details, and the FCN features influence the performance of the SVM classifier. The precision rate, recall rate, and quality index of the proposed method are all better than the compared methods. The following conclusions can be drawn from the experimental results. For the study area with clear features and minimal obstructions, the proposed method can effectively extract buildings from an image. This method can also obtain ideal results for areas with a complex distribution of buildings can also get ideal results. |
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| Keywords: | remote sensing building extraction image segmentation FCN neural network SVM high-resolution remote sensing image |
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