Meta-TR: Meta-Attention Spatial Compressive Imaging Network With Swin Transformer

Abstract

As a flourishing research topic in the field of remote sensing, spatial compressive imaging (SCI) can utilize prior knowledge to recover high-dimensional signals from low-resolution measurements through joint sampling and compression, thus contributing to the bandwidth reduction of information transmission. However, most of the existing SCI methods based on deep learning cannot effectively utilize prior information, and difficult to perform deep extraction of image features, so the reconstruction is not ideal in the case of low sampling ratio. To address the above difficulty, we propose an SCI network based on meta-attention (MA) and swin transformer, named Meta-TR. We adopt the swin transformer as the network backbone, through the wide application of self-attention mechanisms, to achieve deeper extraction of image features, thereby improving the reconstruction quality under low sampling ratios. In addition, we design an MA module, which adopts Squeeze-Excitation architecture to convert the metadata of SCI image degradation process to attention vectors. Then, the attention vectors are used in the channel modulation of network feature maps to guide the network training. Extensive experiments are performed on different benchmark remote sensing datasets and different sampling ratios to confirm the superiority of the proposed Meta-TR method.

Keywords

• Deep learning
• meta-attention (MA)
• remote sensing
• spatial compressive imaging (SCI)
• swin transformer