Deep learning combined with topology and channel features for road extraction from remote sensing images

Ci Gao; Lingjia Gu; Ruizhi Ren; Mingda Jiang

doi:10.1117/12.2631489

30 September 2022 Deep learning combined with topology and channel features for road extraction from remote sensing images

Ci Gao, Lingjia Gu, Ruizhi Ren, Mingda Jiang

Author Affiliations +

Proceedings Volume 12232, Earth Observing Systems XXVII; 122321K (2022) https://doi.org/10.1117/12.2631489
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Using remote sensing images to automatically extract road network information has become an imposing method because manually labeling roads is a time-consuming and laborious task. Traditional road extraction is regarded as a pixel-based segmentation method, which predicts the probability of each pixel on the road. It often neglects the pixel’s neighboring information and the topology property of the road network, so some branches of roads cannot be recognized by this method. To mitigate the problem, this paper proposes a new road extraction method with attention and topology modules, TSELinkNet. It extracts channel features and topology features from the images and then integrates them with spatial features to acquire a comprehensive feature. This method is conducted on two different types of road datasets including urban and rural areas. Also, we compare the predicted results from TSE-LinkNet with other results from existing methods using Precision, Recall, F1 and mean intersection over union (mIoU) evaluation metrics. In both datasets, TSE-LinkNet improves Precision metrics by 0.61% and 2.58% respectively. In the urban road dataset, other metrics arise greatly as well, where Recall, F1 and mIoU increase1.28%, 14.18% and 4.46% sharply. The extracted roads from TSE-LinkNet have better connectivity compared with other methods in qualitative results. Experimental results showed that this method has a satisfying ability to extract roads from complex-topology urban areas.

Citation Download Citation

Ci Gao, Lingjia Gu, Ruizhi Ren, and Mingda Jiang "Deep learning combined with topology and channel features for road extraction from remote sensing images", Proc. SPIE 12232, Earth Observing Systems XXVII, 122321K (30 September 2022); https://doi.org/10.1117/12.2631489

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Roads

Image segmentation

Remote sensing

Convolution

Feature extraction

Neural networks

Binary data

Show All Keywords

Keywords/Phrases

Search In:

Publication Years