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We tackle the problem of image inpainting in the remote sensing domain.Remote sensing images possess high resolution and geographical variations, thatrender the conventional inpainting methods less effective. This further entailsthe requirement of models with high complexity to sufficiently capture thespectral, spatial and textural nuances within an image, emerging from its highspatial variability. To this end, we propose a novel inpainting method thatindividually focuses on each aspect of an image such as edges, colour andtexture using a task specific GAN. Moreover, each individual GAN alsoincorporates the attention mechanism that explicitly extracts the spectral andspatial features. To ensure consistent gradient flow, the model uses residuallearning paradigm, thus simultaneously working with high and low levelfeatures. We evaluate our model, alongwith previous state of the art models, onthe two well known remote sensing datasets, Open Cities AI and Earth on Canvas,and achieve competitive performance.

RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework