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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 87-92     DOI: 10.6046/gtzyyg.2018.02.12
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Object-oriented classification method for oblique photogrammetric point clouds
Xue HE(), Zhengrong ZOU, Yunsheng ZHANG, Shouji DU, Te ZHENG
School of Geosciences and Info-Physics,Central South University, Changsha 410083, China
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Abstract  

With the development of image dense matching method, point clouds can be obtained from multi-view oblique aerial images, whose accuracy and density can be comparable with LiDAR point clouds. However, the currently derived colored point clouds lack classification information. In view of such a situation, this paper proposes an object-based classification method for oblique photogrammetric point clouds. The first step of this method is to calculate features of each point. Then, SLIC algorithm is used to divide the corresponding image into super-pixels. After that, point clouds are clustered into super-voxels as objects according to the relationship between point clouds and images, and features of each object are calculated afterwards. Random forests algorithm is used to classify these super-voxels. Finally, contextual information is adopted to optimize the initial classification results. Two sets of data were employed for evaluating the proposed method, and the overall accuracy could reach up to 91.2% and 88.1% respectively, which improves the precision by 2.3% and 8.2% compared with the point-based classification.
Keywords point clouds classification      features of point clouds      oblique image      object-oriented      random forests     
:  P231  
Issue Date: 30 May 2018
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Xue HE
Zhengrong ZOU
Yunsheng ZHANG
Shouji DU
Te ZHENG
Cite this article:   
Xue HE,Zhengrong ZOU,Yunsheng ZHANG, et al. Object-oriented classification method for oblique photogrammetric point clouds[J]. Remote Sensing for Land & Resources, 2018, 30(2): 87-92.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.12     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/87
Fig.1  Overview flowchart of the method in this paper
Fig.2  Data sources
数据编号 m 点云总体
对象数量		 训练数据集 测试数据集
屋顶 地面 植被 立面 屋顶 地面 植被 立面
第1组 80 000 39 097 111 107 53 111 440 426 210 441
100 000 48 002 136 129 62 133 543 515 247 531
150 000 69 504 195 183 86 184 776 728 340 736
第2组 80 000 41 586 62 52 55 48 245 205 218 192
100 000 51 606 73 63 65 57 292 250 256 227
150 000 76 218 104 88 95 80 415 352 378 316
  
评价指标 第1组 第2组
m=80 000 m=100 000 m=150 000 m=80 000 m=100 000 m=150 000
Kappa系数 0.963 2 0.986 6 0.977 2 0.978 2 0.979 1 0.969 8
分类精度/% 97.30 99.02 98.33 98.37 98.44 97.74
Tab.2  Learning results of classifier
Fig.3  Importance of datasets features
Fig.4  Visualization for classification results
分类方法 第1组 第2组
基于单点的分类方法 88.9 79.9
面向对象的分类方法 90.0 84.9
面向对象的分类方法优化后 91.2 88.1
Tab.3  Overall accuracy(%)
Fig.5  Examples of error classification
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