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

Ma, Fei; Gao, Fei; Sun, Jinping; Zhou, Huiyu; Hussain, Amir

doi:10.3390/rs11212586

Cited by 62 publications

(34 citation statements)

References 30 publications

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“…In addition to the models above, the incorporation between attention mechanisms and neural networks, which aim to improve the robustness of models, is widely studied. Attention Graph Convolution Network (AGCN) [35] consists of an attention mechanism layer and Graph Convolution Networks(GCNs) to perform superpixel-wise segmentation in big SAR imagery data. Graph Attention Model (GAM) [36] focuses on small but informative parts of the graph, avoiding noise in the rest of the graph.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Graph Embedding via Graph Attenuated Attention Networks

Wang

et al. 2020

IEEE Access

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Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, stateof-the-art models are based on graph convolutional neural networks. These models automatically extract features, in combination with the features of the graph model, to generate feature embeddings with a strong expressive ability. However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features. To this end, we propose Graph Attenuated Attention networks(GAATs), a novel representation method, which integrates an attenuated attention mechanism to assign different weight in different relation path and acquire the information from the neighborhoods. As a result, entities and relations can be learned in any neighbors. Our empirical research provides insight into the effectiveness of the attenuated attentionbased models, and we show significant improvement compared to the state-of-the-art methods on two benchmark datasets WN18RR and FB15k-237. INDEX TERMS Knowledge graph embedding, attenuated attention mechanism, neighbor nodes, translational model, graph attention networks.

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Section: Related Workmentioning

confidence: 99%

Knowledge Graph Embedding via Graph Attenuated Attention Networks

Wang

et al. 2020

IEEE Access

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“…Recently, ref. [ 32 ] proposed a novel attention graph convolution network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. However, AGCN is prone to errors when segmenting some geo-objects with a small scale, such as rivers and roads.…”

Section: Related Workmentioning

confidence: 99%

Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Cui

Meng

Hao

et al. 2021

Sensors

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Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

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“…With the development of graph neural networks, they have attracted increasing attention in GNN-based semantic segmentation [10][11][12]. Comparing with the pixel-based semantic segmentation methods, the current GNNs usually take objects as input nodes, in which computational complexity is relatively small.…”

Section: Introductionmentioning

confidence: 99%

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

Cui

Meng

et al. 2021

Remote Sensing

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The pixel-based semantic segmentation methods take pixels as recognitions units, and are restricted by the limited range of receptive fields, so they cannot carry richer and higher-level semantics. These reduce the accuracy of remote sensing (RS) semantic segmentation to a certain extent. Comparing with the pixel-based methods, the graph neural networks (GNNs) usually use objects as input nodes, so they not only have relatively small computational complexity, but also can carry richer semantic information. However, the traditional GNNs are more rely on the context information of the individual samples and lack geographic prior knowledge that reflects the overall situation of the research area. Therefore, these methods may be disturbed by the confusion of “different objects with the same spectrum” or “violating the first law of geography” in some areas. To address the above problems, we propose a remote sensing semantic segmentation model called knowledge and spatial pyramid distance-based gated graph attention network (KSPGAT), which is based on prior knowledge, spatial pyramid distance and a graph attention network (GAT) with gating mechanism. The model first uses superpixels (geographical objects) to form the nodes of a graph neural network and then uses a novel spatial pyramid distance recognition algorithm to recognize the spatial relationships. Finally, based on the integration of feature similarity and the spatial relationships of geographic objects, a multi-source attention mechanism and gating mechanism are designed to control the process of node aggregation, as a result, the high-level semantics, spatial relationships and prior knowledge can be introduced into a remote sensing semantic segmentation network. The experimental results show that our model improves the overall accuracy by 4.43% compared with the U-Net Network, and 3.80% compared with the baseline GAT network.

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

Cited by 62 publications

References 30 publications

Knowledge Graph Embedding via Graph Attenuated Attention Networks

Knowledge Graph Embedding via Graph Attenuated Attention Networks

Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation

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