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Open AccessArticle

Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images

by Jing Zhang 1,†, Shaofu Lin 1,2, Lei Ding 3,* and Lorenzo Bruzzone 3
1
Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing 100022, China
2
Beijing Institute of Smart City, Beijing University of Technology, Chaoyang District, Beijing 100022, China
3
Remote Sensing Laboratory, Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 5, Trento 38122, Italy
*
Author to whom correspondence should be addressed.
Current address: NO.100 Pingle Garden, Chaoyang District, Beijing, China.
Remote Sens. 2020, 12(4), 701; https://doi.org/10.3390/rs12040701
Received: 15 January 2020 / Revised: 8 February 2020 / Accepted: 17 February 2020 / Published: 24 February 2020
(This article belongs to the Special Issue Deep Neural Networks for Remote Sensing Applications)
The semantic segmentation of remote sensing images (RSIs) is important in a variety of applications. Conventional encoder-decoder-based convolutional neural networks (CNNs) use cascade pooling operations to aggregate the semantic information, which results in a loss of localization accuracy and in the preservation of spatial details. To overcome these limitations, we introduce the use of the high-resolution network (HRNet) to produce high-resolution features without the decoding stage. Moreover, we enhance the low-to-high features extracted from different branches separately to strengthen the embedding of scale-related contextual information. The low-resolution features contain more semantic information and have a small spatial size; thus, they are utilized to model the long-term spatial correlations. The high-resolution branches are enhanced by introducing an adaptive spatial pooling (ASP) module to aggregate more local contexts. By combining these context aggregation designs across different levels, the resulting architecture is capable of exploiting spatial context at both global and local levels. The experimental results obtained on two RSI datasets show that our approach significantly improves the accuracy with respect to the commonly used CNNs and achieves state-of-the-art performance. View Full-Text
Keywords: semantic segmentation; convolutional neural network; deep learning; image analysis; remote sensing semantic segmentation; convolutional neural network; deep learning; image analysis; remote sensing
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MDPI and ACS Style

Zhang, J.; Lin, S.; Ding, L.; Bruzzone, L. Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images. Remote Sens. 2020, 12, 701.

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