Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation
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...
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doaj-8efb8c0f4c784609bbbc2af6638128d42021-03-30T23:02:00ZengMDPI AGRemote Sensing2072-42922021-03-01131312131210.3390/rs13071312Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic SegmentationWei Cui0Xin He1Meng Yao2Ziwei Wang3Yuanjie Hao4Jie Li5Weijie Wu6Huilin Zhao7Cong Xia8Jin Li9Wenqi Cui10School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaThe 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.https://www.mdpi.com/2072-4292/13/7/1312remote sensingsemantic segmentationknowledgespatial relationshipspatial pyramid distanceGAT |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Cui Xin He Meng Yao Ziwei Wang Yuanjie Hao Jie Li Weijie Wu Huilin Zhao Cong Xia Jin Li Wenqi Cui |
spellingShingle |
Wei Cui Xin He Meng Yao Ziwei Wang Yuanjie Hao Jie Li Weijie Wu Huilin Zhao Cong Xia Jin Li Wenqi Cui Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation Remote Sensing remote sensing semantic segmentation knowledge spatial relationship spatial pyramid distance GAT |
author_facet |
Wei Cui Xin He Meng Yao Ziwei Wang Yuanjie Hao Jie Li Weijie Wu Huilin Zhao Cong Xia Jin Li Wenqi Cui |
author_sort |
Wei Cui |
title |
Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation |
title_short |
Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation |
title_full |
Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation |
title_fullStr |
Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation |
title_full_unstemmed |
Knowledge and Spatial Pyramid Distance-Based Gated Graph Attention Network for Remote Sensing Semantic Segmentation |
title_sort |
knowledge and spatial pyramid distance-based gated graph attention network for remote sensing semantic segmentation |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-03-01 |
description |
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. |
topic |
remote sensing semantic segmentation knowledge spatial relationship spatial pyramid distance GAT |
url |
https://www.mdpi.com/2072-4292/13/7/1312 |
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