泥石流
中国
特征(语言学)
地理
萃取(化学)
地图学
地质学
碎片
气象学
考古
化学
语言学
色谱法
哲学
作者
Wei Xu,Baoyun Wang,Rui Yuan,Yi Luo,Cunxi Liu
出处
期刊:Photogrammetric Engineering and Remote Sensing
[American Society for Photogrammetry and Remote Sensing]
日期:2024-05-01
卷期号:90 (5): 313-323
标识
DOI:10.14358/pers.23-00078r2
摘要
Debris flow susceptibility evaluation plays a crucial role in the prevention and control of debris flow disasters. Therefore, this article proposes a convolutional neural network model named multi-level feature extraction network (MFENet). First, a dual-channel CNN architecture incorporating the Embedding Channel Attention mechanism is used to extract shallow features from both digital elevation model images and multispectral images. Subsequently, channel shuffle and feature concatenation are applied to the features from the two channels to obtain fused feature sets. Following this, a deep feature extraction is performed on the fused feature sets using a residual module improved by maximum pooling. Finally, the susceptibility index of gullies to debris flows is calculated based on the similarity scores.
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