Gao, L.; Xiao, S.; Hu, C.; Yan, Y. Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network. Appl. Sci.2023, 13, 7143.
Gao, L.; Xiao, S.; Hu, C.; Yan, Y. Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network. Appl. Sci. 2023, 13, 7143.
Gao, L.; Xiao, S.; Hu, C.; Yan, Y. Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network. Appl. Sci.2023, 13, 7143.
Gao, L.; Xiao, S.; Hu, C.; Yan, Y. Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network. Appl. Sci. 2023, 13, 7143.
Abstract
Convolutional neural networks (CNN) have attracted much attention as a commonly used method for hyperspectral image (HSI) classification in recent years, however, CNNs can only be applied to Euclidean data and have limitations in dealing with relationships due to the limitations of local feature extraction. However, each pixel of a hyperspectral image contains a set of spectral bands that are correlated and interact with each other, and the methods used to process Euclidean data cannot effectively obtain these correlations. In contrast, the graph convolutional network (GCN) can be used in non-Euclidean data, but usually leads to oversmoothing and ignoring local detail features due to the need for superpixel segmentation processing to reduce computational effort. To overcome the above problems, we constructed a network a fusion network based on GCN and CNN, which contains two branches: a graph convolutional network based on superpixel segmentation and a convolutional network with added attention mechanism. The graph convolu-tional branch can extract the structural features and capture the relationships between the nodes, and the convolutional branch can extract the detailed features in the local fine region. Owing to the fact that the features extracted from the two branches are different, the classification performance can be improved by fusing the complementary features extracted from the two branches. To vali-date the proposed algorithm, experiments were conducted on three widely used datasets, namely Indian Pines, Pavia University, and Salinas, and the overall accuracy of 98.78% was obtained in the Indian Pines dataset, and the overall accuracy of 98.99% and 98.69% was obtained in the other two datasets. The results showed that the proposed fusion network can obtain richer features and achieve high classification accuracy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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