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Context Aggregation Network for Semantic Labeling in Aerial Images

1
School of Electronic Information, Wuhan University, Wuhan 430072, China
2
The CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1158; https://doi.org/10.3390/rs11101158
Received: 23 April 2019 / Revised: 13 May 2019 / Accepted: 14 May 2019 / Published: 15 May 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Semantic labeling for high resolution aerial images is a fundamental and necessary task in remote sensing image analysis. It is widely used in land-use surveys, change detection, and environmental protection. Recent researches reveal the superiority of Convolutional Neural Networks (CNNs) in this task. However, multi-scale object recognition and accurate object localization are two major problems for semantic labeling methods based on CNNs in high resolution aerial images. To handle these problems, we design a Context Fuse Module, which is composed of parallel convolutional layers with kernels of different sizes and a global pooling branch, to aggregate context information at multiple scales. We propose an Attention Mix Module, which utilizes a channel-wise attention mechanism to combine multi-level features for higher localization accuracy. We further employ a Residual Convolutional Module to refine features in all feature levels. Based on these modules, we construct a new end-to-end network for semantic labeling in aerial images. We evaluate the proposed network on the ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate that our network outperforms other competitors on both datasets with only raw image data. View Full-Text
Keywords: convolutional neural networks; semantic labeling; context aggregation; channel attention; residual convolution; aerial images convolutional neural networks; semantic labeling; context aggregation; channel attention; residual convolution; aerial images
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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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Cheng, W.; Yang, W.; Wang, M.; Wang, G.; Chen, J. Context Aggregation Network for Semantic Labeling in Aerial Images. Remote Sens. 2019, 11, 1158.

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