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首页> 外文会议>Asian conference on remote sensingACRS >UTILIZING DECISION TREE-BASED THRESHOLDS IN RECONSTRUCTING TRAINING DATASETS FOR SINGLE-CLASSIFICATION BINARY SUPPORT VECTOR MACHINE PROBLEM FROM POINT CLOUD DATA
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UTILIZING DECISION TREE-BASED THRESHOLDS IN RECONSTRUCTING TRAINING DATASETS FOR SINGLE-CLASSIFICATION BINARY SUPPORT VECTOR MACHINE PROBLEM FROM POINT CLOUD DATA

机译：利用基于决策树的阈值来重建训练数据集，用于从点云数据的单分类二进制支持向量机问题

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摘要

In some cases of agricultural features extraction, the binary Support Vector Machine (SVM) classification problem only requires the objective of mapping a single class across a relatively large geographic area without the need to identify other land use and land cover classes. While the conduct of ground trothing to collect field data samples is a requirement to achieve high accuracy levels, sometimes it is impractical considering the labor and cost of conducting field surveys that necessitates single species only. As such, the Cocos Nucifera or coconut, which is an agricultural class that is visually discernible through LiDAR-derived Canopy Height Model (CHM), may be classified without the need for in situ data collection. This is done by exploiting the threshold values calculated via Decision Tree (DT) algorithm in reconstructing datasets. In comparison, classification outputs from in situ training samples and from the DT-derived samples achieve similar accuracy levels, hence this study introduces a classification methodology that eliminates the need for field data gathering and manual 'training data" selection in mapping coconut species. A single-classification binary SVM has been implemented using LiDAR-derived CHM, utilizing only the elevation information contained in the point cloud data, with a grid resolution of one meter. The results suggest that automatic selection of samples is tolerable given that a representative calibration site is identified.
机译：在某些情况下，农业特征提取，二元支持向量机（SVM）分类问题只需要在相对较大的地理区域上映射单个类的目的，而无需识别其他土地使用和陆地覆盖类。虽然地面诱饵来收集现场数据样本的行为是实现高精度水平的要求，但有时考虑到执行田间调查的劳动力和成本是不切实际的。因此，通过激光雷达衍生的冠层高度模型（CHM）可视地辨别的Cocos Nucifera或椰子，可以在不需要原位数据收集的情况下进行分类。这是通过利用重构数据集中通过决策树（DT）算法计算的阈值来完成的。相比之下，从原位训练样本和来自DT导出的样本的分类输出实现了类似的准确度水平，因此本研究介绍了一种分类方法，可以消除对映射椰子种类中的现场数据收集和手动“培训数据”选择的分类方法。一个已经使用LIDAR衍生的CHM实现了单分类二进制SVM，仅利用点云数据中包含的升高信息，具有一米的网格分辨率。结果表明，给出了代表校准站点的自动选择样本被确定。

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