关键词: 生成对抗网络, 视觉注意, 遥感图像, 去雾

Abstract: Dehazing algorithms based on image training pairs are difficult to deal with the problems of insufficient training sample pairs in remote sensing images, and have the model with weak generalization ability, therefore, a remote sensing image dehazing method based on cascaded Generative Adversarial Network (GAN) was proposed. In order to solve the missing of paired remote sensing datasets, U-Net GAN (UGAN) learning haze generation and Pixel Attention GAN (PAGAN) learning dehazing were proposed. In the proposed method, UGAN was used to learn how to add haze to the haze-free remote sensing images with the details of the images retained by using unpaired clear and haze image sets, and then was used to guide the PAGAN to learn how to correctly dehazing such images. To reduce the discrepancy between the synthetic haze remote sensing images and the dehazing remote sensing images, the self-attention mechanism was added to PAGAN. By the generator, the high-resolution detail features were generated by using cues from all feature locations in the low-resolution image. By the discriminator, the detail features in distant parts of the images were checked whether they are consistent with each other. Compared with the dehazing methods such as Feature Fusion Attention Network (FFANet), Gated Context Aggregation Network (GCANet) and Dark Channel Prior (DCP), this cascaded GAN method does not require a large number of paired data to train the network repeatedly. Experimental results show this method can remove haze and thin cloud effectively, and is better than the comparison methods on both visual effect and quantitative indices.

Key words: Generative Adversarial Network (GAN), visual attention, remote sensing image, dehazing