Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks

• URL: http://arxiv.org/abs/2008.04021v1
• Date: Mon, 10 Aug 2020 11:00:19 GMT
• Title: Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
• Authors: Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou, Ruili Wang, and Jie Yang
• Abstract summary: We introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. A novel scale-wise architecture is introduced to learn from the multi-level feature maps and improve the semantics of the features. Our model achieves state-of-the-art 78.86 IOU on the Massachusetts dataset with 14.89M parameters and 86.78B FLOPs, with 4x fewer FLOPs but higher accuracy (+3.47% IOU)
• Score: 28.32775611169636
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 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 feature pyramid 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 multi-level 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 datasets 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 dataset 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.

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