Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network's ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively.

中文翻译：

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双路径注意力网用于遥感语义图像分割

语义分割在能够理解遥感图像的内容中起着重要的作用。近年来，基于全卷积网络（FCN）的深度学习方法已被证明对遥感图像的语义分割有效。但是，丰富的信息和复杂的内容使网络训练难以进行细分，并且数据集必然受到约束。在本文中，我们提出了一种称为双路径注意网络（DPA-Net）的卷积神经网络（CNN）模型，该模型具有简单的模块化结构，可以添加到任何细分模型中以增强其学习特征的能力。细分模型附加了两种类型的注意力模块，一种关注空间信息，另一种关注渠道。然后，这两个注意模块的输出融合在一起，进一步提高了网络提取特征的能力，从而有助于获得更精确的细分结果。最后，数据预处理和扩充策略用于补偿少量数据集和分布不均。建议的网络已在高分图像数据集（GID）上进行了测试。结果表明，该网络的平均IoU分别优于U-Net，PSP-Net和DeepLab V3 +，分别为0.84％，2.54％和1.32％。建议的网络已在高分图像数据集（GID）上进行了测试。结果表明，该网络的平均IoU分别优于U-Net，PSP-Net和DeepLab V3 +，分别为0.84％，2.54％和1.32％。建议的网络已在高分图像数据集（GID）上进行了测试。结果表明，该网络的平均IoU分别优于U-Net，PSP-Net和DeepLab V3 +，分别为0.84％，2.54％和1.32％。