3D点云震害建筑物深度学习样本增强方法
Deep learning sample enhancement method for 3D point cloud seismic damaged buildings
- 2023年27卷第8期 页码：1876-1887
纸质出版日期： 2023-08-07
DOI： 10.11834/jrs.20211009
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摘要
针对震后复杂场景下LiDAR点云建筑物破坏类型自动识别问题，为满足应急救援时效性、准确性需求，告别传统人工震害特征提取，充分挖掘点云数据中灾区建筑物震害信息，进一步实现建筑物自动化智能化识别。本文将3D点云深度学习方法应用于建筑物震害识别，构建了包含倒塌、局部倒塌、未倒塌3种建筑物破坏类型的点云数据集。基于PointNet++网络探究了各类别样本量及其均衡性对识别精度的影响，并提出破坏建筑物样本增强方法，丰富了各类别样本点云形态。利用2010年海地7.0级地震后机载LiDAR数据，在PointNet++网络中进行了样本增强前后分类精度比较、样本量以及均衡性分析实验，样本增强后倒塌和局部倒塌的分类精度分别提高近27%和17%，模型整体平均分类精度、Kappa系数均有近15%的提升。实验结果表明三维建筑物震害深度学习模型在各类别样本量足够且均衡时，才能取得较好的分类识别效果。
Abstract
To address the problems in the automatic identification of building damage types in post-earthquake complex lidar point cloud scenes
satisfy the timeliness and accuracy requirements in emergency rescue operations
abandon traditional artificial seismic damage feature extraction methods
fully excavate the seismic damage information of buildings in the disaster area from point cloud data
and further realize an automatic and intelligent recognition of buildings
this paper proposes a building seismic damage recognition model based on the PointNet++ network. This study also establishes collapsed
partially collapsed
and uncollapsed point cloud training datasets that can provide an important scientific basis for earthquake emergency rescue and disaster assessment.
This paper applies the 3D point cloud deep learning method to identify seismic-damaged buildings. Given the uneven sample size
based on the characteristics of the PointNet++ network and the original point cloud sample shape
we propose a sample enhancement method that involves inverse distance interpolation
symmetry
and top projection to increase the amount of collapsed and partially collapsed samples.
Sample enhancement not only increases the number of collapsed and partially collapsed buildings-hence making the samples more comprehensive and diverse-but also solves the problem of uneven samples. Therefore
the classification accuracies of collapse and partial collapse are improved by about 30% and 20%
respectively
and the overall average classification accuracy and kappa coefficient of the model are improved by more than 10%. The differences in classification accuracy between collapsed and uncollapsed and between partially collapsed and uncollapsed are reduced from 40% and 30% to about 15%.
(1) The characteristics of the point cloud samples and network model should be fully considered when building a seismic damage training dataset. In this paper
we assume that PointNet++ has the same learning characteristics for the same geometric shape in different scales and spatial rotation changes. We design a sample enhancement method for the collapse and partial collapse categories that not only increases the number of samples but also enriches their damage form
thus effectively improving the classification accuracy of local collapse.
(2) The sample size and its balance have a huge influence on the recognition effect of the earthquake damage recognition model established by the PointNet++ network. Only when the sample size is sufficient and the number of samples in each category is relatively uniform can a better classification and recognition effect be achieved. However
the sample size is not the decisive factor for improving the classification effect. When the sample size is uniform
the accuracy for the uncollapsed category is still higher than that for the other two categories. The classification effect is also related to other factors
such as sample selection
network design
and internal feature learning methods
which warrant further exploration.
关键词
遥感分类识别PointNet++样本增强LiDAR点云震害建筑物
Keywords
remote sensingclassification and recognitionPointNet++sample enhancementLiDAR point cloudseismic damage buildings
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