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Graph Neural Network via Edge Convolution for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2021.3108883.png)
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Abstract
En 中文
Graph neural network (GNN) has recently gained increasing attention in the hyperspectral image (HSI) classification. Compared with convolutional neural network (CNN), GNN can effectively relieve the scarcity of labeled data. In our method, we first perform feature learning on large-scale irregular regions through GNN and then extract local spatial-spectral features at the pixel level. Besides, we incorporate edge convolution (EdgeConv) into GNN to adaptively capture the interrelationship of the representative descriptors and fully exploit the discriminative features on graph. Experiments on several HSI datasets show that our method can achieve better classification performance compared with the state-of-the-art HSI classification methods.
Keywords:
Convolutional neural networks
Feature extraction
Image edge detection
Message passing
Convolution
Hyperspectral imaging
Time complexity
Convolutional neural network (CNN)
edge convolution (EdgeConv)
graph neural network (GNN)
hyperspectral image (HSI)
Journal
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