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Article

Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

1
Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Academic Editors: Stamatis Kalogirou and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 245; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040245
Received: 25 February 2021 / Revised: 29 March 2021 / Accepted: 3 April 2021 / Published: 7 April 2021
Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning. By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation. Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels. Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest. View Full-Text
Keywords: semantic segmentation; building and road segmentation; self-attention; deep learning semantic segmentation; building and road segmentation; self-attention; deep learning
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MDPI and ACS Style

Ding, C.; Weng, L.; Xia, M.; Lin, H. Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image. ISPRS Int. J. Geo-Inf. 2021, 10, 245. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040245

AMA Style

Ding C, Weng L, Xia M, Lin H. Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image. ISPRS International Journal of Geo-Information. 2021; 10(4):245. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040245

Chicago/Turabian Style

Ding, Cheng; Weng, Liguo; Xia, Min; Lin, Haifeng. 2021. "Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image" ISPRS Int. J. Geo-Inf. 10, no. 4: 245. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040245

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