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Semantic Segmentation of Remote Sensing Images Based on Dual Attention and Multi-scale Feature Fusion

机译：基于双重关注和多尺度特征融合的遥感图像的语义分割

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摘要

We propose a remote sensing image semantic segmentation model based on dual attention and multi -scale feature fusion to solve the problems of objects scale differences and missing small objects. This model uses ResNet50 in the coding part to extract features. First of all, the output features of each stage of ResNet50 are introduced into the pyramid pooling module, making full use of the multi-scale context information of the image to cope with the change of the object scales. Secondly, the dual attention is introduced in the final output features of ResNet50 to establish the semantic relationship between the spatial and channel dimensions, which enhances the ability of feature representation and improve the condition that small targets are difficult to segment. Finally, starting from the output features of the attention module, the features of all levels are gradually integrated to complete decoding to refine the target segmentation edge. The designed comparative experiments results show the effectiveness of the proposed method.
机译：我们提出了一种基于双重关注和多级特征融合的遥感图像语义分割模型，解决了物体缩放差异和缺少小对象的问题。此模型使用编码部分中的ResET50来提取功能。首先，RENET50的每个级的输出功能被引入金字塔池模块，充分利用图像的多尺度上下文信息来应对对象比例的变化。其次，在Reset50的最终输出特征中引入了双重关注，以建立空间和通道尺寸之间的语义关系，这提高了特征表示的能力，提高了小目标难以段的条件。最后，从注意模块的输出特征开始，所有级别的功能逐渐集成以完成解码以改进目标分段边缘。设计的比较实验结果表明了该方法的有效性。

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