Landslide Extraction Using Mask R-CNN with Background-Enhancement Method
Landslide Extraction Using Mask R-CNN with Background-Enhancement Method
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DOI：
10.3390/rs14092206
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Ruilin Yang;Feng Zhang;J. Xia;Chuyi Wu
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作者：
Ruilin Yang;Feng Zhang;J. Xia;Chuyi Wu
由于深度学习技术具有独立的特征学习和强大的计算能力，深度学习方法的应用为基于遥感图像的滑坡提取的准确性和自动化带来了提高。然而在应用中，训练样本的质量往往达不到训练深度网络的要求，导致特征学习不足。此外，一些背景物体（例如河流、裸地、建筑物）与山体滑坡具有相似的形状、颜色和纹理。它们可能会混淆自动任务，造成错误和错过的提取。针对上述问题，提出了一种背景增强方法来丰富样本的复杂性。模型可以通过背景增强样本更有效地学习滑坡和背景物体之间的差异，从而减少对背景物体的错误提取。考虑到灾区环境对滑坡的形成起主导作用，以滑坡诱发属性（DEM、坡度、距河流距离）作为补充，为滑坡提取模型提供附加信息，进一步提高提取结果的准确性。将所提出的方法应用于提取2014年8月云南省鲁甸县发生的山体滑坡，并使用mask R-CNN模型进行了对比实验。使用背景增强样本和滑坡诱发信息的实验显示出令人满意的结果，F1 分数为 89.08%。与仅使用卫星图像作为输入数据的实验的F1分数相比，显着提高了22.38%，强调了我们的背景增强方法的适用性和有效性。
The application of deep learning methods has brought improvements to the accuracy and automation of landslide extractions based on remote sensing images because deep learning techniques have independent feature learning and powerful computing ability. However, in application, the quality of training samples often fails the requirement for training deep networks, causing insufficient feature learning. Furthermore, some background objects (e.g., river, bare land, building) share similar shapes, colors, and textures with landslides. They can be confusing to automatic tasks, contributing false and missed extractions. To solve the above problems, a background-enhancement method was proposed to enrich the complexity of samples. Models can learn the differences between landslides and background objects more efficiently through background-enhanced samples, then reduce false extractions on background objects. Considering that the environments of disaster areas play dominant roles in the formation of landslides, landslide-inducing attributes (DEM, slope, distance from river) were used as supplements, providing additional information for landslide extraction models to further improve the accuracy of extraction results. The proposed methods were applied to extract landslides that occurred in Ludian county, Yunnan Province, in August 2014. Comparative experiments were conducted using a mask R-CNN model. The experiment using both background-enhanced samples and landslide-inducing information showed a satisfying result with an F1 score of 89.08%. Compared with the F1 score from the experiment using only satellite images as input data, it was significantly improved by 22.38%, underscoring the applicability and effectiveness of our background-enhancement method.