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Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery

1
School of Automation & 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
Thermo Fisher Scientific, Richardson, TX 75081, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 20; https://doi.org/10.3390/rs11010020
Received: 17 November 2018 / Revised: 14 December 2018 / Accepted: 19 December 2018 / Published: 22 December 2018
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
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Abstract

Dense semantic labeling is significant in high-resolution remote sensing imagery research and it has been widely used in land-use analysis and environment protection. With the recent success of fully convolutional networks (FCN), various types of network architectures have largely improved performance. Among them, atrous spatial pyramid pooling (ASPP) and encoder-decoder are two successful ones. The former structure is able to extract multi-scale contextual information and multiple effective field-of-view, while the latter structure can recover the spatial information to obtain sharper object boundaries. In this study, we propose a more efficient fully convolutional network by combining the advantages from both structures. Our model utilizes the deep residual network (ResNet) followed by ASPP as the encoder and combines two scales of high-level features with corresponding low-level features as the decoder at the upsampling stage. We further develop a multi-scale loss function to enhance the learning procedure. In the postprocessing, a novel superpixel-based dense conditional random field is employed to refine the predictions. We evaluate the proposed method on the Potsdam and Vaihingen datasets and the experimental results demonstrate that our method performs better than other machine learning or deep learning methods. Compared with the state-of-the-art DeepLab_v3+ our model gains 0.4% and 0.6% improvements in overall accuracy on these two datasets respectively. View Full-Text
Keywords: remote sensing imagery; dense semantic labeling; fully convolutional networks; atrous spatial pyramid pooling; encoder-decoder; superpixel-based DenseCRF remote sensing imagery; dense semantic labeling; fully convolutional networks; atrous spatial pyramid pooling; encoder-decoder; superpixel-based DenseCRF
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, Y.; Liang, B.; Ding, M.; Li, J. Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery. Remote Sens. 2019, 11, 20.

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