Road Detection From Remote Sensing Images by Generative Adversarial Networks | IEEE Journals & Magazine | IEEE Xplore

Road Detection From Remote Sensing Images by Generative Adversarial Networks

Open Access

Proposed model contains a generator and a discriminator. The generator produces the segmentation map. Meanwhile, the discriminator takes the segmentation map or ground tr...

Abstract:

Road detection with high-precision from very high resolution remote sensing imagery is very important in a huge variety of applications. However, most existing approaches...Show More
Topic: Advanced Data Analytics for Large-scale Complex Data Environments

Abstract:

Road detection with high-precision from very high resolution remote sensing imagery is very important in a huge variety of applications. However, most existing approaches do not automatically extract the road with a smooth appearance and accurate boundaries. To address this problem, we proposed a novel end-to-end generative adversarial network. In particular, we construct a convolutional network based on adversarial training that could discriminate between segmentation maps coming either from the ground truth or generated by the segmentation model. The proposed method could improve the segmentation result by finding and correcting the difference between ground truth and result output by the segmentation model. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods greatly on the performance of segmentation map.
Topic: Advanced Data Analytics for Large-scale Complex Data Environments
Proposed model contains a generator and a discriminator. The generator produces the segmentation map. Meanwhile, the discriminator takes the segmentation map or ground tr...
Published in: IEEE Access ( Volume: 6)
Page(s): 25486 - 25494
Date of Publication: 13 November 2017
Electronic ISSN: 2169-3536

Funding Agency:


References

References is not available for this document.