论文标题
数字高程变化检测（DECD）的位置不确定性和质量保证
Positional uncertainty and quality assurance of digital elevation change detection (DECD)
论文作者
论文摘要
由于灾难造成的变化挑战，需要将迫切需要从2D图像（DEM）扩展到数字高程模型（DEM）的快速变化检测研究。这项研究调查了由不同程度的DEM复杂性和DEM误导引起的数字高程变化检测（DECD）的位置不确定性。不幸的是，对于DECD，使用三个sigma规则（3σR）受到参数估计的准确性的干扰，这受到DEM差异样本的异常值（即不同的DEM）的影响。因此，为了减少DECD的上述不确定性，我们提出了一种新的质量保证策略，自适应地审查了三层统计规则（AC3σR），其中，通过审查样品审查的样品，逐步估算平均估计的标准偏差以外的全球DEM差异样本的离群值是矩估计的。 Compared with the 3σR and censored three-sigma rule (C3σR) that is similar to AC3σR but without iteration for both simulation and real-world data experiments, the proposed global AC3σR method always exhibits the highest accuracies of DECD in terms of both the overall accuracies 0.99967, 0.98740 and kappa coefficients 0.99598, 0.81803 respectively, and the strongest在模拟的最大注册误差和最复杂的地形复杂性条件下，具有较大收敛间隔的鲁棒性[0，0.30010]。
Studies on rapid change detection of large area urgently need to be extended from 2D image to digital elevation model (DEM) due to the challenge of changes caused by disasters. This research investigates positional uncertainty of digital elevation change detection (DECD) caused by different degrees of DEM complexity and DEM misregistration. Unfortunately, using three-sigma rule (3σR) for DECD is disturbed by accuracy of parameter estimation, which is affected by the outliers (i.e., varied DEM) from DEM differencing samples. Hence, to reduce the aforementioned uncertainty of DECD, we propose a new strategy of quality assurance, adaptively censored three-sigma rule (AC3σR), in which with the samples censored, outliers of global DEM differencing samples outside the standard deviations of the mean calculated by moment estimation are iteratively removed. Compared with the 3σR and censored three-sigma rule (C3σR) that is similar to AC3σR but without iteration for both simulation and real-world data experiments, the proposed global AC3σR method always exhibits the highest accuracies of DECD in terms of both the overall accuracies 0.99967, 0.98740 and kappa coefficients 0.99598, 0.81803 respectively, and the strongest robustness with a large convergence interval [0, 0.30010] under the simulated maximum registration error and most complex terrain complexity conditions.
