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 MoreMetadata
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.
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)