Edge Prediction Net for Reconstructing Road Labels Contaminated by Clouds

Extracting road information from remote sensing images has been a popular issue for decades. However, most studies focus on cloudless datasets, without considering cloud occlusion. The thick clouds especially make it impossible to extract the road information from the blocked parts. To address this problem, we propose a new two-stage method. Since generative adversarial networks (GAN) have powerful image generation capabilities, the two-stage method comprises an edge prediction net relied on GAN and a color filling part. The edge prediction net sketches the contours of the region contaminated by thick clouds in road labels, and the second part fills colors in the missing part by using the edges predicted at the first stage. We evaluate our model over the DeepGlobe Road Extraction dataset. The results show that our model performs excellently on visual effects and evaluation indicators.