Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness
Synergistic use of lidar and color aerial photography for mapping urban parcel imperviousness
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DOI：
10.14358/pers.69.9.973
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发表时间：
2003-09-01
影响因子：
1.3
通讯作者：
Archer, CM
中科院分区：
文献类型：
--
作者：
Hodgson, ME;Jensen, JR;Archer, CM
使用高空间分辨率数字化彩色正射摄影和从多回波激光雷达数据提取的地表覆盖高度来绘制和评估地块的不渗透性。使用最大似然分类、谱聚类和专家系统方法从数据集中提取不渗透信息。分类像素（或片段）被聚合到地块中，基于正射摄影和激光雷达导出的表面覆盖高度的分类模型为所有地块产生了不透水的表面结果，其误差在参考数据的 15% 以内。基于规则的每像素模型的标准误差为 7.15%，最大观察误差为 18.94%。每像素最大似然分类产生的标准误差较低，为 6.62%，最大值为 14.16%。每像素最大似然模型的回归斜率（即 0.955）表明观察到的不渗透性与预测的不渗透性之间存在近乎完美的关系。使用基于规则的分类的每段方法的额外努力导致了稍微更好的标准误差（5.85％）和近乎完美的回归斜率（1.016）。
The imperviousness of land parcels was mapped and evaluated using high spatial resolution digitized color orthophotography and surface-cover height extracted from multiple-return lidar data. Maximum-likelihood classification, spectral clustering, and expert system approaches were used to extract the impervious information from the datasets. Classified pixels (or segments) were aggregated to parcels, The classification model based on the use of both the orthophotography and lidar-derived surface-cover height yielded impervious surface results for all parcels that were within 15 percent of reference data. The standard error for the rule-based per-pixel model was 7.15 percent with a maximum observed error of 18.94 percent. The maximum-likelihood per-pixel classification yielded a lower standard error of 6.62 percent with a maximum of 14.16 percent. The regression slope (i.e., 0.955) for the maximum-likelihood per-pixel model indicated a near perfect relationship between observed and predicted imperviousness. The additional effort of using a per-segment approach with a rule-based classification resulted in slightly better standard error (5.85 percent) and a near-perfect regression slope (1.016).