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VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data | IEEE Journals & Magazine | IEEE Xplore

VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data


The workflow of the proposed road extraction model from aerial and Google earth imagery. Road Extraction Stage from Aerial and Google Earth Imagery.

Abstract:

One of the most important tasks in the advanced transportation systems is road extraction. Extracting road region from high-resolution remote sensing imagery is challengi...Show More

Abstract:

One of the most important tasks in the advanced transportation systems is road extraction. Extracting road region from high-resolution remote sensing imagery is challenging due to complicated background such as buildings, trees shadows, pedestrians and vehicles and rural road networks that have heterogeneous forms with low interclass and high intraclass differences. Recently, deep learning-based techniques have presented a notable enhancement in the image segmentation results, however, most of them still cannot preserve boundary information and obtain high-resolution road segmentation map when processing the remote sensing imagery. In the present study, we introduce a new deep learning-based convolutional network called VNet model to produce a high-resolution road segmentation map. Moreover, a new dual loss function called cross-entropy-dice-loss (CEDL) is defined that synthesize cross-entropy (CE) and dice loss (DL) and consider both local information (CE) and global information (DL) to decrease the class imbalance influence and improve the road extraction results. The proposed VNet+CEDL model is implemented on two various road datasets called Massachusetts and Ottawa datasets. The suggested VNet+CEDL approach achieved an average F1 accuracy of 90.64% for Massachusetts dataset and 92.41% for Ottawa dataset. When compared to other state-of-the-art deep learning-based frameworks like FCN, Segnet and Unet, the proposed approach could improve the results to 1.09%, 2.45% and 0.39%, for Massachusetts dataset and 7.21%, 1.86% and 2.68%, for Ottawa dataset. Also, we compared the proposed method with the state-of-the-art road extraction techniques, and the results proved that the proposed technique outperformed other deep learning-based techniques in road extraction.
The workflow of the proposed road extraction model from aerial and Google earth imagery. Road Extraction Stage from Aerial and Google Earth Imagery.
Published in: IEEE Access ( Volume: 8)
Page(s): 179424 - 179436
Date of Publication: 25 September 2020
Electronic ISSN: 2169-3536

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