Multiscale Road Extraction in Remote Sensing Images
Joint Authors
Wulamu, Aziguli
Shi, Zuxian
Zhang, Dezheng
He, Zheyu
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-10
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation.
Among the successful CNN-based methods, U-Net has achieved exciting performance.
In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field.
On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images.
Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points.
Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.
American Psychological Association (APA)
Wulamu, Aziguli& Shi, Zuxian& Zhang, Dezheng& He, Zheyu. 2019. Multiscale Road Extraction in Remote Sensing Images. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129391
Modern Language Association (MLA)
Wulamu, Aziguli…[et al.]. Multiscale Road Extraction in Remote Sensing Images. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1129391
American Medical Association (AMA)
Wulamu, Aziguli& Shi, Zuxian& Zhang, Dezheng& He, Zheyu. Multiscale Road Extraction in Remote Sensing Images. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129391
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129391