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Subhashini L, Li Y, Zhang J, Atukorale AS. Assessing the effectiveness of a three-way decision-making framework with multiple features in simulating human judgement of opinion classification. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2021.102823] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Xu H, Hou R, Fan J, Zhou L, Yue H, Wang L, Liu J. The unordered time series fuzzy clustering algorithm based on the adaptive incremental learning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179601] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Huanchun Xu
- School of Electronic Information Engineering, Tianjin University, Tianjin, PRC
| | - Rui Hou
- School of Economics and Management, North China Electric Power University, Beijing, PRC
| | - Jinfeng Fan
- Internet Department of State Grid Co., Ltd., Beijing, PRC
| | - Liang Zhou
- China Electric Power Research Institute, Institute of Information and Communication, Beijing, PRC
| | - Hongxuan Yue
- State Grid Xuji Wind Power Technology Co., Ltd., Xuchang, PRC
| | - Liusheng Wang
- State Grid Xuji Wind Power Technology Co., Ltd., Xuchang, PRC
| | - Jiayue Liu
- China Mobile Communications Group Qinghai Co., Ltd., PRC
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Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data. REMOTE SENSING 2019. [DOI: 10.3390/rs11212586] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
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.
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