Landslide Remote Sensing Image Recognition Based on EfficientNet: Taking Bijie City, Guizhou Province as an Example
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摘要: 近年来，随着工程建设的快速发展，工程活动改变了边坡原始地质条件，导致滑坡灾害频繁发生，严重威胁人民的生命财产安全。因此，深入研究滑坡的快速、精确识别方法对于防灾减灾具有重要意义。本文提出一种基于EfficientNet 高效网络提取滑坡深度特征的潜在滑坡识别方法，该方法通过寻找一组最优的复合系数从深度、宽度、分辨率三个维度对神经网络进行扩展，自适应地优化网络结构，并引入带动量的梯度下降算法（Stochastic Gradient Descent Momentum，SGDM）作为网络学习的优化器，充分考虑历史梯度的影响，在参数更新过程中不断调整当前梯度值，从而相应地调整参数的更新幅度，改善神经网络的学习效果，提取滑坡体的深层次特征。实验结果表明，EfficientNet 模型在测试集上的平均准确度达到92.78%，可以高效准确地实时提取滑坡信息，对灾后的快速反应有指导意义。
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关键词:
- 滑坡识别 /
- 深度特征 /
- EfficientNet /
- 带动量的梯度下降算法（SGDM）
Abstract: In recent years, with the rapid development of engineering construction, engineering activities have changed the original geological conditions of slopes, resulting in frequent landslide disasters, which seriously threaten people's life and property safety. Therefore, it is of great significance to study the rapid and accurate identification method of landslides for disaster prevention and reduction. In this paper, a potential landslide recognition method is proposed based on EfficientNet to realize the extraction of landslide depth features. The method extends the neural network from three dimensions of depth, width, and resolution by searching for a set of optimal composite coefficients, and adaptively optimizes the network structure. The Stochastic Gradient Descent Momentum (SGDM) is introduced as the optimizer of network learning, which fully considers the influence of historical gradient. And the current gradient value is constantly adjusted during the param eter updating process, so as to adjust the parameter updating amplitude accordingly, improve the learning effect of neural networks and extract the deep features of the slope. The experimental results show that the average accuracy of the EfficientNet model on the test set reaches 92.78%, which can efficiently and accurately extract landslide information in real-time and provides guiding information for the rapid response after the disaster. -
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