| 多特征分量结合的WorldView-3影像建筑容积率分类提取 |
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| 引用本文： | 叶敏,王斌,王思远,刘长征,李艳霞,岑炜.多特征分量结合的WorldView-3影像建筑容积率分类提取[J].武汉大学学报(信息科学版),2019,44(11):1674-1684. |
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| 作者姓名： | 叶敏 王斌 王思远 刘长征 李艳霞 岑炜 |
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| 作者单位： | 1.北京交通大学土木建筑工程学院, 北京, 100044 |
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| 基金项目： | 国家自然科学基金91547107国家自然科学基金41428103新疆生产建设兵团科技攻关项目2015AD018 |
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| 摘 要： | 城市现状建筑容积率的分类提取对于有效把握城市用地开发强度以及制定科学合理的控制性详细规划具有重要参考意义。提出了一种主成分分量、主方向、边界指数以及矩形拟合度等多特征分量相结合的超高分辨率卫星影像建筑容积率贝叶斯分类提取方法。基于分类结果，采用阴影面积法与阴影长度法计算容积率并进行精度对比验证。利用WorldView-3卫星影像进行提取实验，并对实验区建筑逐一进行实地调查，结果表明，在容积率计算中，阴影面积法总体精度为93.90%，阴影长度法总体精度为85.19%，阴影面积法较阴影长度法在容积率分类提取精度上优势更突出。
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| 关 键 词： | 容积率 阴影 建筑物 特征分量 精度分析 |
| 收稿时间： | 2018-08-16 |
Extracting Floor Area Ratio of the Classified Buildings from Very High Resolution Satellite Image Using Multiple Features |
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| Affiliation: | 1.School of Civil Engineering and Architecture, Beijing Jiaotong University, Beijing 100044, China2.Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China3.Chinese Academy of Sciences University, Beijing 100045, China4.School of Information Science and Technology, Shihezi University, Shihezi 832003, China |
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| Abstract: | With the rapid development of urbanization, problems such as shortage of land resource and low efficiency of land use have emerge and grown increasingly serious. The extraction of floor area ratio of the classified buildings in urban areas is of great significance to the floor area ratio(FAR) management and regulatory detailed planning in urban land development. Moreover, high-resolution data which provide analysis results of high precision are under comprehensive application in various lines of work, especially in the field of territorial resources. Given these, an object-based method for FAR extraction from very high resolution satellite image using multiple features, which are principal components, main direction, border index and rectangular fit, and Bayesian classifier is proposed. Meanwhile, FAR extraction results calculated via shadow area and shadow length are compared. We applied this method to a clip of WorldView-3 image and validated the FAR results of building units included one by one. Experimental results show that the average accuracy for shadow area method is 93.90% and 85.19% for shadow length method and shadow area method is more effective than shadow length method. |
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