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Open AccessArticle

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

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School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
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Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
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Cognitive Big Data and Cyber-Informatics (CogBID) Laboratory, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2586; https://doi.org/10.3390/rs11212586
Received: 2 September 2019 / Revised: 6 October 2019 / Accepted: 30 October 2019 / Published: 4 November 2019
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels as processing units. However, the over-segmented images become non-Euclidean structure data that traditional deep Convolutional Neural Networks (CNN) cannot directly process. Here, we propose a novel Attention Graph Convolution Network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. AGCN consists of an attention mechanism layer and Graph Convolution Networks (GCN). GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification. The attention mechanism layer is introduced to guide the graph convolution layers to focus on the most relevant nodes in order to make decisions by specifying different coefficients to different nodes in a neighbourhood. The attention layer is located before the convolution layers, and noisy information from the neighbouring nodes has less negative influence on the attention coefficients. Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches. Its computation time is also far less than the current mainstream pixel-level semantic segmentation networks. View Full-Text
Keywords: big data; Synthetic Aperture Radar (SAR); segmentation; Graph Convolution Network (GCN); attention mechanism; neighbourhood consistency big data; Synthetic Aperture Radar (SAR); segmentation; Graph Convolution Network (GCN); attention mechanism; neighbourhood consistency
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Ma, F.; Gao, F.; Sun, J.; Zhou, H.; Hussain, A. Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data. Remote Sens. 2019, 11, 2586.

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