Remote Sensing Image Scene Classification Method Based on Multi-Scale Cyclic Attention Network
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摘要: 高分辨率遥感影像场景分类一直是遥感领域的研究热点.针对遥感场景对尺度的需求具有多样性的问题，提出了一种基于多尺度循环注意力网络的遥感影像场景分类方法.首先，通过Resnet50提取遥感影像多个尺度的特征，采用注意力机制得到影像不同尺度下的关注区域，对关注区域进行裁剪和缩放并输入到网络.然后，融合原始影像不同尺度的特征及其关注区域的影像特征，输入到全连接层完成分类预测.此分类方法在UC Merced Land-Use和NWPU-RESISC45公开数据集上进行了验证，平均分类精度较基础模型Resnet50分别提升了1.89%和2.70%.结果表明，多尺度循环注意力网络可以进一步提升遥感影像场景分类的精度.Abstract: Scene classification of high-resolution remote sensing images has always been a research hotspot in the field of remote sensing. In view of the diversity of scale requirements of remote sensing scenes, in this paper it proposes a remote sensing image scene classification method based on multi-scale cyclic attention network. Firstly, the features of multiple scales of remote sensing scene image are extracted by Resnet50 network, the attention mechanism is used to obtain the region of interest of the image, and the region of interest is clipped and scaled. Then, the features of different scales of the original image and the features of different scale cropped images are fused, input to the full connection layer for classification prediction. The proposed method is validated in UC Merced Land-Use and NWPU-RESISC45, the average classification accuracy is improved by 1.89% and 2.70% respectively compared with Resnet50.The results show that the multi-scale cyclic attention network can further improve the accuracy of remote sensing image scene classification.
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Key words:
- remote sensing /
- scene classification /
- multi-scale /
- convolutional neural network /
- attention mechanism
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图 6 不同尺度组合的图像训练网络的分类精度变化曲线（UC Merced Land-Use）
Fig. 6. Classification accuracy curve of image training network with different scale combinations (UC Merced Land-Use)
图 7 不同尺度组合的图像训练网络的分类精度变化曲线图（NWPU-RESISC45）
Fig. 7. Classification accuracy curve of image training network with different scale combinations (NWPU-RESISC45)
图 8 在UC Merced Land-Use数据集上的类别间错分率对比
a.单尺度模型的混淆矩阵（OA=98.1%）；b. 多尺度模型的混淆矩阵（OA=98.57%）
Fig. 8. Comparison of misclassification rates between categories on UCM dataset
图 9 在UC Merced Land-Use数据集上的易混淆类别
Fig. 9. Misclassified samples on UC Merced Land-Use dataset
图 10 在NWPU-RESISC45数据集上的类别间错分率对比
a.单尺度模型的混淆矩阵（OA= 90.62%）；b.多尺度模型的混淆矩阵（OA= 91.18%）
Fig. 10. Comparison of misclassification rates between categories on NWPU dataset
表 1 Resnet50网络配置
Table 1. Resnet50 network configuration
layer name 50-layer Conv1 7×7, 64, stride 2 Conv2_x 3×3 Max Pool, stride 2 $ \left[\begin{array}{c}1\times \mathrm{1, 64}\\ 3\times \mathrm{3, 64}\\ 1\times \mathrm{1, 256}\end{array}\right] $ × 3 Conv3_x $ \left[\begin{array}{c}1\times \mathrm{1, 128}\\ 3\times \mathrm{3, 128}\\ 1\times \mathrm{1, 512}\end{array}\right] $× 4 Conv4_x $ \left[\begin{array}{c}1\times \mathrm{1, 256}\\ 3\times \mathrm{3, 256}\\ 1\times \mathrm{1, 1}\mathrm{ }024\end{array}\right] $ × 6 Conv5_x $ \left[\begin{array}{c}1\times \mathrm{1, 512}\\ 3\times \mathrm{3, 512}\\ 1\times \mathrm{1, 2}\mathrm{ }048\end{array}\right] $ × 3 GAP, k-d FC, softmax 
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表 2 两个数据集的相关信息
Table 2. Information about two datasets
Datasets Scene Images per class Total images Sizes Training rate UC Merced Land-Use 21 100 2 100 256×256 80% NWPU-RESISC45 45 700 31 500 256×256 10%
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表 3 基于UC Merced Land-Use不同尺度特征的分类精度
Table 3. Classification accuracy of different scale features on UCM dataset
number scale A-OA (%) 1 S_128_256 97.85$ \pm $0.67 2 S_160_256 98.10$ \pm $0.39 3 S_192_256 98.51$ \pm $0.11 4 S_224_256 98.33$ \pm $00.14 5 S_256 98.18$ \pm $00.09 6 S_288_256 98.10$ \pm $00.39
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表 4 基于NWPU-RESISC45不同尺度特征的分类精度
Table 4. Classification accuracy of different scale features on NWPU-RESISC45 dataset
number scale A-OA (%) 1 S_128_256 91.04$ \pm $0.03 2 S_160_256 90.86$ \pm $0.19 3 S_192_256 91.18$ \pm $0.02 4 S_224_256 90.19$ \pm $0.31 5 S_256 90.25$ \pm $0.20 6 S_288_256 90.85$ \pm $0.27
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表 5 不同方法对UC Merced Land-Use的分类精度
Table 5. Classification accuracy of different methods for UC Merced Land-Use
Method OA (%) BoVW（Yang and Newsam, 2010） 76.80 GoogleNet（Nogueira et al., 2017） 92.80 CaffeNet（Xia et al., 2017） 95.02$ \pm $0.81 Resnet50（Zhang et al., 2019） 96.62$ \pm $0.26 GLM16（Yuan et al., 2019） 94.97$ \pm $1.16 VGG-VD16+MSCP（He et al., 2018） 98.36$ \pm $0.58 AlexNet + MSCP（He et al., 2018） 97.29$ \pm $0.63 The model of this paper 98.51$ \pm $0.11
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表 6 不同方法对NWPU-RESISC45的分类精度
Table 6. Classification accuracy of different methods for NWPU-RESISC45
Method OA (%) BoVW（Cheng et al., 2017） 41.72$ \pm $0.21 Fine-tuned AlexNet（Cheng et al., 2017） 81.22$ \pm $0.19 Fine-tuned GoogleNet (Cheng et al., 2017) 82.57$ \pm $0.12 Fine-tuned VGGNet-16(Cheng et al., 2017) 87.15$ \pm $0.45 Resnet50（Zhao et al., 2020） 88.48$ \pm $0.21 VGG-VD16+MSCP（He et al., 2018） 85.33$ \pm $0.17 AlexNet + MSCP（He et al., 2018） 81.70$ \pm $0.23 The model of this paper 91.18$ \pm $0.02
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