Remote Sensing Image Super-Resolution via Residual Aggregation and Split Attentional Fusion Network

Remote sensing images contain various land surface scenes and different scales of ground objects, which greatly increases the difficulty of super-resolution tasks. The existing deep learning-based methods cannot solve this problem well. To achieve high-quality super-resolution of remote sensing images, a residual aggregation and split attentional fusion network (RASAF) is proposed in this article. It is mainly divided into the following three parts. First, a split attentional fusion block is proposed. It uses a basic split-fusion mechanism to achieve cross-channel feature group interaction, allowing the method to adapt to various land surface scene reconstructions. Second, to fully exploit multiscale image information, a hierarchical loss function is used. Third, residual learning is used to reduce the difficulty of training in super-resolution tasks. However, the respective residual branch features are used quite locally and fail to represent the real value. A residual aggregation mechanism is used to aggregate the local residual branch features to generate higher quality local residual branch features. The comparison of RASAF with some classical super-resolution methods using two widely used remote sensing datasets showed that the RASAF achieved better performance. And it achieves a good balance between performance and model parameter number. Meanwhile, the RASAF's ability to support multilabel remote sensing image classification tasks demonstrates its usefulness.

Automated summary

The article introduces a residual aggregation and split attentional fusion network (RASAF) for high-quality super-resolution of remote sensing images. RASAF utilizes split attentional fusion and residual aggregation mechanisms to fully exploit multiscale image information for improved performance. It also demonstrates practicality in remote sensing image classification tasks.