Soil loss is a global environmental problem that can intensively damage surrounding ecosystems. To control soil loss and secure agricultural activities, check dams are constructed for soil conservation. However, due to ineffective management, many check dams are abandoned and are highly prone to be damaged due to rainstorms. Such a phenomenon would cause more serious damage to surrounding environments than that associated with common soil loss. The similar basic signatures of check dam areas and their surroundings can blur the boundaries of these structures in images and negatively affect boundary identification, thereby limiting the effectiveness of traditional check dam area extraction techniques based on the pixel level or visual inspection. To facilitate the extraction of check dams, we propose a method that integrates deep learning and object‐based image analysis. We select the Loess Plateau, on which several effective check dam systems have been constructed in recent years to address intense soil loss, as the study area on which to perform high‐resolution imagery experiments to determine the influences of different sample combinations. The parameters influencing the segmentation algorithm are also examined to determine the best parameter combination for the extraction of check dams. Four test areas comprising 12 check dams across different environments were selected with which to test the accuracy of the proposed method. In addition, we compared the check dam extraction capabilities of our proposed method with those of the random forest and deep learning approaches. The results show that the proposed method can achieve a classification accuracy and kappa coefficient that signify good performance in detecting the boundaries and areas of check dams. The proposed method generally outperforms the random forest and deep learning techniques. The extraction results can support the efficient soil management and guide future studies on gully erosion.

中文翻译：

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基于对象的图像分析和深度学习的集成，从高分辨率图像中提取检查坝区域

土壤流失是一个全球性的环境问题，会严重破坏周围的生态系统。为了控制水土流失和确保农业活动，建造了水坝以保护土壤。但是，由于管理不善，许多防洪坝被废弃，很容易因暴雨而遭到破坏。这种现象对周围环境的破坏要比与普通土壤流失有关的破坏更为严重。止水坝区域及其周围环境的相似基本特征会模糊图像中这些结构的边界，并对边界识别产生负面影响，从而限制了基于像素级别或视觉检查的传统止水坝区域提取技术的有效性。为了方便提取止水坝，我们提出了一种将深度学习和基于对象的图像分析相集成的方法。我们选择黄土高原作为研究区域，在该区域上进行了高分辨率的图像实验以确定不同样本组合的影响，黄土高原是近年来为解决严重的土壤流失而构建的几种有效的防洪坝系统。还检查了影响分割算法的参数，以确定提取止水坝的最佳参数组合。选择了四个测试区域，其中包括在不同环境中的12个止水坝，用以测试所提出方法的准确性。此外，我们将我们提出的方法与随机森林和深度学习方法的检查坝提取能力进行了比较。结果表明，所提出的方法可以达到分类精度和卡伯系数，表明在检查坝的边界和面积方面具有良好的性能。所提出的方法通常优于随机森林和深度学习技术。提取结果可以支持有效的土壤管理，并指导今后关于沟壑侵蚀的研究。