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Intersection of adversarial learning and satellite image processing is anemerging field in remote sensing. In this study, we intend to address synthesisof high resolution multi-spectral satellite imagery using adversarial learning.Guided by the discovery of attention mechanism, we regulate the process of bandsynthesis through spatio-spectral Laplacian attention. Further, we useWasserstein GAN with gradient penalty norm to improve training and stability ofadversarial learning. In this regard, we introduce a new cost function for thediscriminator based on spatial attention and domain adaptation loss. Wecritically analyze the qualitative and quantitative results compared withstate-of-the-art methods using widely adopted evaluation metrics. Ourexperiments on datasets of three different sensors, namely LISS-3, LISS-4, andWorldView-2 show that attention learning performs favorably againststate-of-the-art methods. Using the proposed method we provide an additionaldata product in consistent with existing high resolution bands. Furthermore, wesynthesize over 4000 high resolution scenes covering various terrains toanalyze scientific fidelity. At the end, we demonstrate plausible large scalereal world applications of the synthesized band.

S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attention for Multi-Spectral Band Synthesis