Global digital elevation model correction method for coastal areas based on active learning
To address issues such as neglecting sample quality in traditional global digital elevation model(GDEM)correction methods,this paper proposed a GDEM correction method for coastal areas based on active learning.Firstly,a certain number of representative sample points were selected as the initial training set.Then,high-quality representative points were iteratively selected for model training through a clustering-based batch processing sampling algorithm.Finally,a machine learning model was constructed by using all selected representative points to achieve GDEM correction.The accuracy of the model was validated by selecting mean square error and mean absolute error with the coastal areas of Jacksonville and Charleston in the United States as the training area and transfer experimental area respectively.Experimental results show that,compared with traditional GDEM correction methods,the proposed method only requires 8.57%of the sampling points to complete model training.The root mean square error of GDEM is reduced by 3.31%to 51.65%and the mean absolute error is reduced by 4.76%to 48.72%.In the transfer experiment area,the root mean square error of the corrected COPDEM30 is reduced from 6.52 m to 1.68 m.Compared with traditional methods,the root mean square error and mean absolute error of the proposed method are reduced by at least 24.82%and 30.28%respectively,demonstrating that the model has a certain level of transferability.
active learningglobal digital elevation model(GDEM)correctioncoastal area
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