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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model
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Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model

机译：使用Dempster-Shafer模型组合的两个主要深度学习卷积神经网络流的滑坡映射

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摘要

Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causes the river to thrust upward. The EQIL inventories are generated mostly by the traditional or semisupervised mapping approaches, which required a parameter's tuning or binary threshold decision in the practical application. In this study, we investigated the impact of optical data from the PlanetScope sensor and topographic factors from the ALOS sensor on EQIL mapping using a deep-learning convolution neural network (CNN). Thus, six training datasets were prepared and used to evaluate the performance of the CNN model using only optical data and using these data along with each and all topographic factors across the west coast of the Trishuli river in Nepal. For the first time, the Dempster–Shafer (D–S) model was applied for combining the resulting maps from each CNN stream that trained with different datasets. Finally, seven different resulting maps were compared against a detailed and accurate inventory of landslide polygons by a mean intersection-over-union (mIOU). Our results confirm that using the training dataset of the spectral information along with the topographic factor of the slope is helpful to distinguish the landslide bodies from other similar features, such as barren lands, and consequently increases the mapping accuracy. The improvement of the mIOU was a range from approximately zero to more than 17%. Moreover, the D–S model can be considered as an optimizer method to combine the results from different scenarios.
机译：除了地震的直接危害之外，河床中沉积的地震诱导的滑坡（欧准人）导致河流向上推动。 EQIL库存主要由传统或半熟的映射方法产生，这在实际应用中需要参数的调谐或二进制阈值决定。在这项研究中，我们研究了使用深度学习卷积神经网络（CNN）的EQIL映射上的Planetscope传感器和地形因素的光学数据的影响。因此，准备了六个训练数据集并用于评估CNN模型的性能使用光学数据，并使用这些数据以及在尼泊尔的Trishuli河西海岸的各个和所有地形因素。首次，应用Dempster-Shafer（D-S）模型用于将所得到的映射与由不同数据集接受训练的每个CNN流组合。最后，将七种不同的由此产生的地图与平均交叉联盟（Miou）进行了一个详细的和准确的滑坡多边形库存。我们的结果证实，使用频谱信息的训练数据集以及斜率的地形因子有助于将滑坡体与其他类似特征区分开，例如贫瘠的陆地，因此增加了映射精度。 Miou的改善是从大约零到17％的范围。此外，D-S模型可以被认为是与不同方案组合结果的优化方法。

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