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摘要:
针对泥石流灾害沟谷图像分类问题，文章对Resnet18网络进行改进，提出了一种改进的卷积神经网络模型。通过在网络结构中加入残差注意力模块，解决了原模型提取图像特征较差、边缘模糊的问题，改进后的网络能精确捕捉到泥石流灾害沟谷图像中的轮廓和内部山脊信息。此外，文章还对多种注意力机制结构进行了实验对比，分析其差异性，得出最适合泥石流灾害沟谷数据分类的注意力机制网络。实验表明改进后的网络模型在泥石流灾害沟谷图像的分类准确率达到75.42%，其分类性能在Resnet18网络模型的基础上提升了5.1%。
Abstract:For debris flow disasters in valleys image classification problems, this paper improved the Resnet18 network, an improved convolution neural network model is put forward, through adding residual attention in network structure module, solved the original model to extract the image features to solve the problem of poor, edge model accurately capture the debris flow disasters in valleys in the image contour and internal ridge information.In addition, this paper also conducts comparative experiments on various attention mechanism structures, analyzes their differences, and obtains the attention mechanism network most suitable for debris flow disaster gully data.The experimental results show that the classification accuracy of the improved network model in debris flow disaster gullies reaches 75.42%, and its classification performance is improved by 5.1% compared with the Resnet18 network model.
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Key words:
- Resnet18 /
- attention mechanism /
- remote sensing image /
- debris flow disaster
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表 1 Alexnet与Alexnet_CBAM结果对比
Table 1. Comparison of Alexnet and Alexnet CBAM results
模型 特异性/% 灵敏度/% 损失 准确率/% Alexnet 60.37 60.08 0.0394 61.29 Alexnet_CBAM 61.81 62.15 0.0373 63.44 
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表 2 VGG16与VGG16_CBAM结果对比
Table 2. Comparison of VGG16 and VGG16_CBAM results
模型 特异性/% 灵敏度/% 损失 准确率/% VGG16 61.41 61.52 0.0371 62.72 VGG16_CBAM 60.76 62.86 0.0356 65.23
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表 3 Resnet18与Resnet18_CBAM结果对比
Table 3. Comparison of Resnet18 and Resnet18_CBAM results
模型 特异性/% 灵敏度/% 损失 准确率/% Resnet18 87.26 69.88 0.0289 70.32 Resnet18_CBAM 87.99 71.35 0.0261 73.12
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表 4 不同注意力机制模块结果对比
Table 4. Comparison of results of different attentional mechanism modules
模型 参数量/106 时间 准确度/% 损失 Resnet18_C 11.27 24 m 17 s 70.56 0.0269 Resnet18_S 11.27 24 m 11 s 71.17 0.0280 Resnet18_CS 11.27 22 m 42 s 73.12 0.0266 Resnet18_SC 11.27 21 m 31 s 75.42 0.0248
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