Multiresolution generative adversarial networks with bidirectional adaptive-stage progressive guided fusion for remote sensing image

Remote sensing image(RSI)with concurrently high spatial,temporal,and spectral resolutions cannot be produced by a single sensor.Multisource RSI fusion is a convenient technique to realize high spatial resolution multispectral(MS)images(spatial spectral fusion,i.e.SSF)and high temporal and spatial resolution MS images(spatiotemporal fusion,i.e.STF).Currently,deep learning-based fusion models can only implement SSF or STF,lacking models that perform both SSF and STF.Multiresolution generative adversarial networks with bidirectional adaptive-stage progressive guided fusion(BAPGF)for RSI are proposed to implement both SSF and STF,namely BPF-MGAN.A bidirectional adaptive-stage feature extraction architecture infine-scale-to-coarse-scale and coarse-scale-to-fine-scale modes is introduced.The designed BAPGF introduces a previous fusion result-oriented cross-stage-level dual-residual attention fusion strategy to enhance critical information and suppress superfluous information.Adaptive resolution U-shaped discriminators are implemented to feed multiresolution context into the generator.A generalized multitask loss function unlimited by no-reference images is developed to strengthen the model via constraints on the multiscale feature,structural,and content similarities.The BPF-MGAN model is validated on SSF datasets and STF datasets.Compared with the state-of-the-art SSF and STF models,results demonstrate the superior performance of the proposed BPF-MGAN model in both subjective and objective evaluations.