  The processing and recognition of geoscience images have wide applications.
Most of existing researches focus on understanding the high-quality geoscience
images by assuming that all the images are clear. However, in many real-world
cases, the geoscience images might contain occlusions during the image
acquisition. This problem actually implies the image inpainting problem in
computer vision and multimedia. To the best of our knowledge, all the existing
image inpainting algorithms learn to repair the occluded regions for a better
visualization quality, they are excellent for natural images but not good
enough for geoscience images by ignoring the geoscience related tasks. This
paper aims to repair the occluded regions for a better geoscience task
performance with the advanced visualization quality simultaneously, without
changing the current deployed deep learning based geoscience models. Because of
the complex context of geoscience images, we propose a coarse-to-fine
encoder-decoder network with coarse-to-fine adversarial context discriminators
to reconstruct the occluded image regions. Due to the limited data of
geoscience images, we use a MaskMix based data augmentation method to exploit
more information from limited geoscience image data. The experimental results
on three public geoscience datasets for remote sensing scene recognition,
cross-view geolocation and semantic segmentation tasks respectively show the
effectiveness and accuracy of the proposed method.
