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首页> 外文会议>Asian conference on remote sensing;ACRS >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：利用决策树的阈值重建点云数据中单分类二进制支持向量机问题的训练数据集

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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：在某些农业特征提取情况下，二进制支持向量机（SVM）分类问题仅需要在相对较大的地理区域上映射单个类别的目标，而无需识别其他土地用途和土地覆盖类别。虽然要达到高精度水平，必须进行地面修井以收集田间数据样本，但考虑到仅需要单个物种进行田间调查的劳力和成本，有时这是不切实际的。因此，可以通过LiDAR派生的树冠高度模型（CHM）在视觉上辨别可可椰子或椰子，这是一种农业类别，无需现场数据收集即可分类。这是通过在重建数据集中利用通过决策树（DT）算法计算的阈值来完成的。相比之下，原位训练样本和DT衍生样本的分类输出达到相似的准确度水平，因此，本研究引入了一种分类方法，该方法无需在绘制椰子树种时就需要现场数据收集和手动选择“训练数据”。使用LiDAR衍生的CHM实现单分类二进制SVM，仅利用点云数据中包含的高程信息，网格分辨率为1米，结果表明，在有代表性的校准位点的情况下，可以自动选择样品被识别。

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