Coarse-to-fine Task-driven Inpainting for Geoscience Images

TL;DR

This paper introduces a coarse-to-fine inpainting method tailored for geoscience images, improving occlusion repair to enhance both visualization and task performance without altering existing models.

Contribution

It proposes a novel coarse-to-fine encoder-decoder network with adversarial discriminators and a MaskMix data augmentation to address occlusions in geoscience images for better task accuracy.

Findings

Effective inpainting on three public geoscience datasets

Improves geoscience task performance with occlusion repair

Outperforms existing inpainting methods in accuracy

Abstract

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.