AFFNet: adversarial feature fusion network for super-resolution image reconstruction in remote sensing images

Abstract

As an important source of Earth surface information, remote sensing image has the problems of rough and fuzzy image details and poor perception quality, which affect further analysis and application of geographic information. To address the above problems, we introduce the adversarial feature fusion network with an attention-based mechanism for super-resolution reconstruction of remote sensing images in this paper. First, residual structures are designed in the generator to enhance the deep feature extraction capability of remote sensing images. The residual structure is composed of the depthwise over-parameterized convolution and self-attention mechanism, which work synergistically to extract deep feature information from remote sensing images. Second, coordinate attention feature fusion module is introduced at the feature fusion stage, which can fuse shallow features and deep high-level features. Therefore, it can enhance the attention of the model to different features and better fuse inconsistent semantic features. Finally, we design the pixel-attention upsampling module in the up-sampling stage. It adaptively focuses on the most information-rich regions of a pixel and restores the image details more accurately. We conducted extensive experiments on several remote sensing image datasets, and the results showed that compared with current advanced models, our method can better restore the details in the image and achieve good subjective visual effects, which also verifies the effectiveness and superiority of the algorithm proposed in this paper.