Deep neural networks have been successfully used in image processing and have benefitted image compression technology. On this basis, deeper and more extensive use of compression algorithms in remote sensing applications was investigated in our study. As a general residual block does not effectively utilize the connections between channels and spatial regions in the reconstruction process, an attention mechanism codec framework based on the channel-spatial attention residual block was introduced. Consequently, the network can account for the interdependence between channels and enhance specific spatial areas. Simultaneously, the feature importance map makes the network focus on valuable information, especially for small objects and remote sensing images with many tiny details, which is crucial to describe their contours. Introducing the attention mechanism commonly used in target detection can effectively recover the missing detailed information for remote sensing compressed images. Moreover, to utilize the underlying information of the various scene categories and characterize the feature differences between scenes in remote sensing images, an adaptive scene-aware module was introduced to enhance the feature representation in remote sensing images of different scenes. The experimental results show that the proposed method has superior subjective and objective effects in remote sensing image compression tasks.

中文翻译：

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用于遥感图像压缩的自适应场景感知深度注意力网络

深度神经网络已成功应用于图像处理，并有益于图像压缩技术。在此基础上，我们的研究调查了压缩算法在遥感应用中的更深入和更广泛的使用。由于一般的残差块在重建过程中没有有效利用通道和空间区域之间的连接，引入了一种基于通道空间注意力残差块的注意力机制编解码框架。因此，网络可以解释通道之间的相互依赖性并增强特定的空间区域。同时，特征重要性图使网络专注于有价值的信息，特别是对于小物体和具有许多微小细节的遥感图像，这对于描述它们的轮廓至关重要。引入目标检测中常用的注意力机制，可以有效地恢复遥感压缩图像缺失的详细信息。此外，为了利用各种场景类别的底层信息并表征遥感图像中场景之间的特征差异，引入了自适应场景感知模块来增强不同场景遥感图像中的特征表示。实验结果表明，该方法在遥感图像压缩任务中具有优越的主客观效果。为了利用各种场景类别的底层信息并表征遥感图像中场景之间的特征差异，引入自适应场景感知模块来增强不同场景遥感图像中的特征表示。实验结果表明，该方法在遥感图像压缩任务中具有优越的主客观效果。为了利用各种场景类别的底层信息并表征遥感图像中场景之间的特征差异，引入自适应场景感知模块来增强不同场景遥感图像中的特征表示。实验结果表明，该方法在遥感图像压缩任务中具有优越的主客观效果。