Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification
Most graph-based networks utilize superpixel generation methods as a preprocessing step, considering superpixels as graph nodes. In the case of hyperspectral images having high variability in spectral features, considering an image region as a graph node may degrade the class discrimination ability...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1623 |
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| author | Maryam Imani Daniele Cerra |
| author_facet | Maryam Imani Daniele Cerra |
| author_sort | Maryam Imani |
| collection | DOAJ |
| description | Most graph-based networks utilize superpixel generation methods as a preprocessing step, considering superpixels as graph nodes. In the case of hyperspectral images having high variability in spectral features, considering an image region as a graph node may degrade the class discrimination ability of networks for pixel-based classification. Moreover, most graph-based networks focus on global feature extraction, while both local and global information are important for pixel-based classification. To deal with these challenges, superpixel-based graphs are overruled in this work, and a Graph-based Feature Fusion (GF2) method relying on three different graphs is proposed instead. A local patch is considered around each pixel under test, and at the same time, global anchors with the highest informational content are selected from the entire scene. While the first graph explores relationships between neighboring pixels in the local patch and the global anchors, the second and third graphs use the global anchors and pixels of the local patch as nodes, respectively. These graphs are processed using graph convolutional networks, and their results are fused using a cross-attention mechanism. The experiments on three hyperspectral benchmark datasets show that the GF2 network has high classification performance compared to state-of-the-art methods, while imposing a reasonable number of learnable parameters. |
| format | Article |
| id | doaj-art-aeb3e706f5e04b358548e39e734036a8 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-aeb3e706f5e04b358548e39e734036a82025-08-20T01:49:28ZengMDPI AGRemote Sensing2072-42922025-05-01179162310.3390/rs17091623Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and ClassificationMaryam Imani0Daniele Cerra1Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran P.O. Box 14115-111, IranRemote Sensing Technology Institute, German Aerospace Center (DLR), Muenchener Strasse 20, 82234 Wessling, GermanyMost graph-based networks utilize superpixel generation methods as a preprocessing step, considering superpixels as graph nodes. In the case of hyperspectral images having high variability in spectral features, considering an image region as a graph node may degrade the class discrimination ability of networks for pixel-based classification. Moreover, most graph-based networks focus on global feature extraction, while both local and global information are important for pixel-based classification. To deal with these challenges, superpixel-based graphs are overruled in this work, and a Graph-based Feature Fusion (GF2) method relying on three different graphs is proposed instead. A local patch is considered around each pixel under test, and at the same time, global anchors with the highest informational content are selected from the entire scene. While the first graph explores relationships between neighboring pixels in the local patch and the global anchors, the second and third graphs use the global anchors and pixels of the local patch as nodes, respectively. These graphs are processed using graph convolutional networks, and their results are fused using a cross-attention mechanism. The experiments on three hyperspectral benchmark datasets show that the GF2 network has high classification performance compared to state-of-the-art methods, while imposing a reasonable number of learnable parameters.https://www.mdpi.com/2072-4292/17/9/1623graph convolutional networkattention mechanismfeature fusionhyperspectral image classification |
| spellingShingle | Maryam Imani Daniele Cerra Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification Remote Sensing graph convolutional network attention mechanism feature fusion hyperspectral image classification |
| title | Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification |
| title_full | Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification |
| title_fullStr | Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification |
| title_full_unstemmed | Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification |
| title_short | Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification |
| title_sort | triple graph convolutional network for hyperspectral image feature fusion and classification |
| topic | graph convolutional network attention mechanism feature fusion hyperspectral image classification |
| url | https://www.mdpi.com/2072-4292/17/9/1623 |
| work_keys_str_mv | AT maryamimani triplegraphconvolutionalnetworkforhyperspectralimagefeaturefusionandclassification AT danielecerra triplegraphconvolutionalnetworkforhyperspectralimagefeaturefusionandclassification |