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Use of active learning for earthquake damage mapping from UAV photogrammetric point clouds

机译：主动学习在无人机摄影测量点云中用于地震破坏测绘

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摘要

This article presents an effective classification method for earthquake damage mapping from unmanned aerial vehicles (UAV) photogrammetric point clouds. The classification method consists of three main components: (a) construction of a point feature descriptor regarding to spectral, textural, and geometrical features, (b) optimization of collecting informative training samples through an active learning (AL) method, and (c) fine-tuning the point-based classification results with contextual information. Besides using existing spectral and geometrical features, we design a textural feature based on fractal theory to construct a point feature descriptor through linear combination. A batch-model AL method called Margin Sampling and Multiclass Level Uncertainty (MS-MCLU) is proposed based on classification uncertainty using a Support Vector Machine classifier. We use a multi-label Markov random fields to fine-tune the point-based classification results with a pairwise model. The proposed method was tested using three sets of point clouds generated from UAV images over Mirabello, Lushan, and Wenchuan earthquake scenarios in 2012, Italy, and in 2013 and 2008, China, respectively. The proposed classification method was compared with that of two other feature descriptors, i.e. spectral combined with textural features (Spe_Tex) and geometrical features (Geo). The results show that classification accuracies were improved by using the proposed point feature descriptor. Results also show that the proposed MS-MCLU AL method evidently saved the cost of collecting informative training samples and produced higher classification accuracies than a random sampling strategy. Moreover, contextual information contributed to the improvement on the point-based classification results and was suggested to be considered in earthquake damage mapping applications.
机译：本文提出了一种有效的分类方法，用于对来自无人机（UAV）摄影测量点云的地震伤害进行映射。分类方法由三个主要部分组成：（a）构建关于光谱，纹理和几何特征的点特征描述符；（b）通过主动学习（AL）方法优化收集信息训练样本的方法；以及（c）使用上下文信息微调基于点的分类结果。除了使用现有的光谱和几何特征外，我们还基于分形理论设计纹理特征，以通过线性组合构造点特征描述符。基于分类不确定性，使用支持向量机分类器，提出了一种批处理模型的AL方法，称为边际抽样和多类水平不确定性（MS-MCLU）。我们使用多标签马尔可夫随机字段，以成对模型微调基于点的分类结果。使用分别从2012年意大利，2013年和2008年中国Mirabello，庐山和汶川地震场景的无人机图像生成的三组点云，测试了该方法。将提出的分类方法与其他两个特征描述符的分类方法进行了比较，即光谱结合纹理特征（Spe_Tex）和几何特征（Geo）。结果表明，使用提出的点特征描述符可以提高分类精度。结果还表明，与随机抽样策略相比，所提出的MS-MCLU AL方法明显节省了收集信息性培训样本的成本，并产生了更高的分类精度。此外，上下文信息有助于改进基于点的分类结果，并建议在地震破坏地图应用程序中加以考虑。

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  • 来源
    《International journal of remote sensing》 |2018年第16期|5568-5595|共28页
  • 作者

    Xu Zhihua; Wu Lixin; Zhang Zhenxin;

  • 作者单位

    China Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing, Peoples R China;

    Cent S Univ, Sch Geosci & Infophys, Changsha 410006, Hunan, Peoples R China;

    Capital Normal Univ, Adv Innovat Ctr Imaging Technol, Beijing, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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