High-quality remote sensing images are difficult to obtain due to limited conditions and high cost for data acquisition. With the development of machine vision and deep learning, some image generation methods (e.g., GANs) are introduced into this field, but it’s still hard to generate images with good texture details and structural dependencies. We establish Skip Attention Mechanism to deal with this problem, which learns dependencies between local points on low-resolution feature maps, and then upsample the attention map and combine it with high-resolution feature maps. With this method, long-range dependencies learned from low-resolution are used for generating remote sensing images with more structural details. We name this method as Skip Attention GAN, which is the first method applying cross-scale attention mechanism for unsupervised remote sensing image generation. Experiments show that our method outperforms previous methods under several metrics. Visual and ablation results of attention layers show that Skip Attention has learned long-distance structural dependencies between similar targets.
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