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Open AccessArticle

Real-Time Dense Semantic Labeling with Dual-Path Framework for High-Resolution Remote Sensing Image

by Yuhao Wang 1,2, Chen Chen 3, Meng Ding 4 and Jiangyun Li 1,2,*
1
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
3
Department of Electrical and Computer Engineering, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
4
Thermo Fisher Scientific, Richardson, TX 75081, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 3020; https://doi.org/10.3390/rs11243020
Received: 7 November 2019 / Revised: 6 December 2019 / Accepted: 7 December 2019 / Published: 14 December 2019
Dense semantic labeling plays a pivotal role in high-resolution remote sensing image research. It provides pixel-level classification which is crucial in land cover mapping and urban planning. With the recent success of the convolutional neural network (CNN), accuracy has been greatly improved by previous works. However, most networks boost performance by involving too many parameters and computational overheads, which results in more inference time and hardware resources, while some attempts with light-weight networks do not achieve satisfactory results due to the insufficient feature extraction ability. In this work, we propose an efficient light-weight CNN based on dual-path architecture to address this issue. Our model utilizes three convolution layers as the spatial path to enhance the extraction of spatial information. Meanwhile, we develop the context path with the multi-fiber network (MFNet) followed by the pyramid pooling module (PPM) to obtain a sufficient receptive field. On top of these two paths, we adopt the channel attention block to refine the features from the context path and apply a feature fusion module to combine spatial information with context information. Moreover, a weighted cascade loss function is employed to enhance the learning procedure. With all these components, the performance can be significantly improved. Experiments on the Potsdam and Vaihingen datasets demonstrate that our network performs better than other light-weight networks, even some classic networks. Compared to the state-of-the-art U-Net, our model achieves higher accuracy on the two datasets with 2.5 times less network parameters and 22 times less computational floating point operations (FLOPs). View Full-Text
Keywords: remote sensing image; real-time dense semantic labeling; convolutional neural networks; light-weight remote sensing image; real-time dense semantic labeling; convolutional neural networks; light-weight
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MDPI and ACS Style

Wang, Y.; Chen, C.; Ding, M.; Li, J. Real-Time Dense Semantic Labeling with Dual-Path Framework for High-Resolution Remote Sensing Image. Remote Sens. 2019, 11, 3020.

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