WSGAN: An Improved Generative Adversarial Network for Remote Sensing Image Road Network Extraction by Weakly Supervised Processing

Abstract

Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The pair segmentation images are not easy to prepare. Thus, to alleviate the burden of acquiring large quantities of training images, this study designed an improved generative adversarial network to extract road networks through a weakly supervised process named WSGAN. The proposed method is divided into two steps: generating the mapping image and post-processing the binary image. During the generation of the mapping image, unlike other road extraction methods, this method overcomes the limitations of manually annotated segmentation images and uses mapping images that can be easily obtained from public data sets. The residual network block and Wasserstein generative adversarial network with gradient penalty loss were used in the mapping network to improve the retention of high-frequency information. In the binary image post-processing, this study used the dilation and erosion method to remove salt-and-pepper noise and obtain more accurate results. By comparing the generated road network results, the Intersection over Union scores reached 0.84, the detection accuracy of this method reached 97.83%, the precision reached 92.00%, and the recall rate reached 91.67%. The experiments used a public dataset from Google Earth screenshots. Benefiting from the powerful prediction ability of GAN, the experiments show that the proposed method performs well at extracting road networks from remote sensing images, even if the roads are covered by the shadows of buildings or trees.

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

“…WSGAN selects ResNet as the backbone network and employs the Patch GAN as GAN'S discriminator. It is believed that WSGAN can recover clearer mapped images in the occluded areas [96]. Zhang et al propose the multi-supervised generative adversarial network (MsGAN), which focuses on the impact of occlusion and shadows on road extraction and learns how to reconstruct the occluded road based on the relationship between the visible road areas and the road centerlines [28].…”

Coastline detection method of remote sensing image based on local information fuzzy clustering

“…The data scale of remote sensing images is large. In the process of image processing, it is necessary to analyze and understand, enhance, denoise, restore, reconstruct, compress and process the corresponding image areas, so as to obtain more important feature information in the image [3]. However, in the process of using remote sensing technology to detect the coastline, it is often affected by the ocean, sediment, reclamation and other factors, which reduces the detection accuracy [4].…”

Target feature matching method of remote sensing image of power tower supported by grid coding

“…The power tower can effectively maintain power facilities, detect power failures, and improve the operational reliability of the power system. At present, most substations lack effective power tower detection technology, and the detection accuracy is low [3], which is not conducive to the effective operation of the power system. Therefore, it is necessary to use remote sensing images for effective feature matching to ensure the detection accuracy of power towers.…”