Feature relevance assessment for the semantic interpretation of 3D point cloud data
Feature relevance assessment for the semantic interpretation of 3D point cloud data
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DOI：
10.5194/isprsannals-ii-5-w2-313-2013
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
M. Weinmann;B. Jutzi;C. Mallet
中科院分区：
文献类型：
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作者：
M. Weinmann;B. Jutzi;C. Mallet
摘要。大型三维点云的自动分析是摄影测量、遥感和计算机视觉领域的一项重要任务。在本文中，我们提出了一种涉及特征相关性评估的点云语义解释新方法，以减少处理时间和内存消耗。给定一个包含130万个3D点的标准基准数据集，我们首先提取21个几何3D和2D特征。随后，我们应用了一个与分类器无关的排序过程，该过程涉及一个一般的相关度量，以获得紧凑和鲁棒的通用特征子集，这些特征通常适用于各种后续任务。该度量基于7种不同的特征选择策略，因此处理给定数据的不同内在属性。对于语义解释3D点云数据的示例，我们展示了使用4种不同的最先进的分类器仅由最相关的特征组成的较小子集的巨大潜力。结果表明，与其包含尽可能多的特征来弥补知识的不足，不如只使用少数通用特征就可以完成场景解释等关键任务，甚至可以提高准确性。
Abstract. The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.