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Optimal scale extraction of farmland in coal mining areas with high groundwater levels based on visible light images from an unmanned aerial vehicle (UAV)
Earth Science Informatics ( IF 2.8 ) Pub Date : 2020-08-06 , DOI: 10.1007/s12145-020-00493-2
Xiao Hu , Xinju Li , Xiangyu Min , Beibei Niu

The accurate acquisition of information concerning farmland in coal mining areas with high groundwater levels can provide a basis for land dynamic monitoring and protection. In this study, visible light images from an unmanned aerial vehicle (UAV) were used as the data source, from which farmland located in the coal mining areas with high groundwater levels were extracted. Based on the optimal scale for image segmentation, which was determined to be 44, farmland was extracted using a sample-based, object-oriented extraction method and a feature combination-based hierarchical extraction method. The results showed that the Kappa coefficient of the latter was 0.87, the correct rate was 88%, the commission was 24%, and the omission was 12%; all of these were better than the corresponding results obtained using the sample-based, object-oriented extraction method. The accuracy of the hierarchical extraction method was verified using the images of the verification area. For these images, the Kappa coefficient of the feature combination-based hierarchical method was 0.96, the correct rate was 95%, the commission was 20%, and the omission was 5%; these were also better than the corresponding values obtained using sample-based, object-oriented extraction. Therefore, this study demonstrates that at a segmentation scale of 44, the hierarchical extraction method based on feature combination not only accurately extract farmland information from the mining area but also have better extraction accuracy than the traditional extraction method. This study thus provides a method and reference basis for the accurate extraction of information concerning ground objects in coal mining areas.



中文翻译:

基于无人飞行器(UAV)的可见光图像,对高地下水位煤矿区的农田进行最佳规模提取

准确获取有关地下水位高的煤矿区耕地的信息,可以为土地动态监测和保护提供基础。在这项研究中,将无人飞行器(UAV)的可见光图像用作数据源,从中提取了地下水位高的煤矿区的农田。基于确定为44的最佳图像分割比例,使用基于样本的,面向对象的提取方法和基于特征组合的分层提取方法来提取农田。结果表明,后者的Kappa系数为0.87,正确率为88%,佣金为24%,遗漏率为12%;所有这些都比使用基于样本的方法获得的相应结果更好,面向对象的提取方法。使用验证区域的图像验证了分层提取方法的准确性。对于这些图像,基于特征组合的分层方法的Kappa系数为0.96,正确率为95%,佣金为20%,遗漏率为5%;这些也比使用基于样本的,面向对象的提取获得的相应值更好。因此,本研究表明,在分割规模为44的情况下,基于特征组合的分层提取方法不仅可以准确地从矿区中提取耕地信息,而且提取精度也要高于传统提取方法。

更新日期:2020-08-08
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