In recent decades, spatial classification has received considerable attention in a wide array of disciplines. In practice, binary response variable is often subject to measurement error, misclassification. To account for the misclassified response in spatial classification, we proposed validation data-based adjustment methods that use interval validation data to rectify misclassified responses. Regression calibration and multiple imputation methods are utilized to correct the misclassified outcomes at the locations where the gold-standard device is not available. Generalized linear mixed model and indicator Kriging are applied for spatial classification at unsampled locations. Simulation studies are performed to compare the proposed methods with naive methods that ignore the misclassification. It was found that the proposed models significantly improve prediction accuracy. Additionally, the proposed models are applied for precipitation detection in South Korea.

近几十年来，空间分类在许多学科中受到了广泛的关注。在实践中，二元响应变量经常会出现测量误差、错误分类。为了解决空间分类中错误分类的响应，我们提出了基于验证数据的调整方法，该方法使用区间验证数据来纠正错误分类的响应。利用回归校准和多重插补方法来纠正无法使用黄金标准设备的位置的错误分类结果。广义线性混合模型和指标克里金法应用于未采样位置的空间分类。进行模拟研究以将所提出的方法与忽略错误分类的简单方法进行比较。结果发现，所提出的模型显着提高了预测精度。此外，所提出的模型还应用于韩国的降水检测。