基于尺度注意力网络的遥感图像场景分类

针对卷积神经网络（CNN）平等地对待输入图像中潜在的对象信息和背景信息，而遥感图像场景又存在许多小对象和背景复杂的问题，提出一种基于注意力机制和多尺度特征变换的尺度注意力网络模型。首先，开发一个快速有效的注意力模块，基于最优特征选择生成注意力图；然后，在ResNet50网络结构的基础上嵌入注意力图，增加多尺度特征融合层，并重新设计全连接层，构成尺度注意力网络；其次，利用预训练模型初始化尺度注意力网络，并使用训练集对模型进行微调；最后，利用微调后的尺度注意力网络对测试集进行分类预测。该方法在实验数据集AID上的分类准确率达到95.72%，与ArcNet方法相比分类准确率提高了2.62个百分点；在实验数据集NWPU-RESISC上分类准确率达到92.25%，与IORN方法相比分类准确率提高了0.95个百分点。实验结果表明，所提方法能够有效提高遥感图像场景分类准确率。

Remote sensing image scene classification based on scale-attention network

The Convolutional Neural Network (CNN) treats the potential object information and background information equally in the input image. However, there are many small objects and complex background in remote sensing scene images. To solve the problem above, a scale-attention network was proposed based on attention mechanism and multi-scale feature transformation. Firstly, a fast and effective attention module was developed, and the attention map was generated based on optimal feature selection. Then, with the attention map embedded, the multi-scale feature fusion layer added and the fully connected layer redesigned on the basis of ResNet50 network, a scale attention network was proposed. Secondly, the pre-training model was used to initialize the scale-attention network, and the training set was employed for the fine-tuning of the network. Finally, the fine-tuned scale-attention network was used to realize the classification prediction of test set. The classification accuracy of the proposed method on the AID scene dataset is 95.72%, which is 2.62 percentage points higher than that of ArcNet. On the NWPU-RESISC scene dataset, this method achieves classification accuracy of 92.25%, 0.95 percentage points higher than that of IORN （Improved Oriented Response Network）. The experimental results demonstrate that the proposed method is able to improve the classification accuracy of remote sensing image scenes.