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Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network (CNN) Streams Combined by the Dempster—Shafer (DS) model
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-01-01 , DOI: 10.1109/jstars.2020.3043836
Omid Ghorbanzadeh , Sansar Raj Meena , Hejar Shahabi Sorman Abadi , Sepideh Tavakkoli Piralilou , Lv Zhiyong , Thomas Blaschke

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.

中文翻译:

使用 Dempster-Shafer (DS) 模型结合的两个主要深度学习卷积神经网络 (CNN) 流的滑坡映射

除了地震的直接危害之外,河床中沉积的地震诱发的滑坡 (EQIL) 质量会导致河流向上推力。EQIL 清单主要由传统或半监督映射方法生成,在实际应用中需要对参数进行调整或二元阈值决策。在这项研究中,我们使用深度学习卷积神经网络 (CNN) 研究了来自 PlanetScope 传感器的光学数据和来自 ALOS 传感器的地形因素对 EQIL 映射的影响。因此,准备了六个训练数据集并用于仅使用光学数据评估 CNN 模型的性能,并将这些数据与尼泊尔 Trishuli 河西海岸的每个和所有地形因素一起使用。首次,Dempster-Shafer (D-S) 模型用于组合来自每个 CNN 流的结果地图,这些地图使用不同的数据集进行训练。最后,通过平均交叉点(mIOU)将七种不同的结果地图与详细而准确的滑坡多边形清单进行了比较。我们的结果证实,使用光谱信息的训练数据集以及坡度的地形因素有助于将滑坡体与其他类似特征(如贫瘠土地)区分开来,从而提高制图精度。mIOU 的改进范围从大约零到超过 17%。此外,D-S 模型可以被视为一种优化器方法,用于组合不同场景的结果。最后,通过平均交叉点(mIOU)将七种不同的结果地图与详细而准确的滑坡多边形清单进行了比较。我们的结果证实,使用光谱信息的训练数据集以及坡度的地形因素有助于将滑坡体与其他类似特征(如贫瘠土地)区分开来,从而提高制图精度。mIOU 的改进范围从大约零到超过 17%。此外,D-S 模型可以被视为一种优化器方法,用于组合不同场景的结果。最后,通过平均交叉点(mIOU)将七种不同的结果地图与详细而准确的滑坡多边形清单进行了比较。我们的结果证实,使用光谱信息的训练数据集以及坡度的地形因素有助于将滑坡体与其他类似特征(如荒地)区分开来,从而提高制图精度。mIOU 的改进范围从大约零到超过 17%。此外,D-S 模型可以被视为一种优化器方法,用于组合不同场景的结果。我们的结果证实,使用光谱信息的训练数据集以及坡度的地形因素有助于将滑坡体与其他类似特征(如贫瘠土地)区分开来,从而提高制图精度。mIOU 的改进范围从大约零到超过 17%。此外,D-S 模型可以被视为一种优化器方法,用于组合不同场景的结果。我们的结果证实,使用光谱信息的训练数据集以及坡度的地形因素有助于将滑坡体与其他类似特征(如贫瘠土地)区分开来,从而提高制图精度。mIOU 的改进范围从大约零到超过 17%。此外,D-S 模型可以被视为一种优化器方法,用于组合不同场景的结果。
更新日期:2021-01-01
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