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Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks

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journal contribution
posted on 08.09.2020, 15:24 by Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou, Ruili Wang, Jie Yang
Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or high costs of manual annotation. To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. We incorporate a feature pyramid (FP) network into generative adversarial networks to minimize the difference between the source and target domains. A generator is learned to produce quality synthetic images, and the discriminator attempts to distinguish them. We also propose a FP network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales' objects. Indeed, a novel scale-wise architecture is introduced to learn from the multilevel feature maps and improve the semantics of the features. For optimization, the model is trained by a joint reconstruction loss function, which minimizes the difference between the fake images and the real ones. A wide range of experiments on three data sets prove the superior performance of the proposed approach in terms of accuracy and efficiency. In particular, our model achieves state-of-the-art 78.86 IOU on the Massachusetts data set with 14.89M parameters and 86.78B FLOPs, with 4x fewer FLOPs but higher accuracy (+3.47% IOU) than the top performer among state-of-the-art approaches used in the evaluation.

History

Citation

IEEE Transactions on Geoscience and Remote Sensing, 2020, https://doi.org/10.1109/TGRS.2020.3016086

Author affiliation

School of Informatics

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Geoscience and Remote Sensing

Publisher

Institute of Electrical and Electronics Engineers

issn

0196-2892

Acceptance date

11/08/2020

Copyright date

2020

Available date

21/08/2020

Language

en

Publisher version

https://ieeexplore.ieee.org/abstract/document/9173823