CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification
Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct flexible...
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| Vydáno v: | IEEE transactions on geoscience and remote sensing Ročník 59; číslo 10; s. 8657 - 8671 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0196-2892, 1558-0644 |
| On-line přístup: | Získat plný text |
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| Abstract | Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions; hence, the HSI spatial contextual structure can be better modeled. However, to reduce the computational complexity and promote the semantic structure learning of land covers, GCN usually works on superpixel-based nodes rather than pixel-based nodes; thus, the pixel-level spectral-spatial features cannot be captured. To fully leverage the advantages of the CNN and GCN, we propose a heterogeneous deep network called CNN-enhanced GCN (CEGCN), in which CNN and GCN branches perform feature learning on small-scale regular regions and large-scale irregular regions, and generate complementary spectral-spatial features at pixel and superpixel levels, respectively. To alleviate the structural incompatibility of the data representation between the Euclidean data-oriented CNN and non-Euclidean data-oriented GCN, we propose the graph encoder and decoder to propagate features between image pixels and graph nodes, thus enabling the CNN and GCN to collaborate in a single network. In contrast to other GCN-based methods that encode HSI into a graph during preprocessing, we integrate the graph encoding process into the network and learn edge weights from training data, which can promote the node feature learning and make the graph more adaptive to HSI content. Extensive experiments on three data sets demonstrate that the proposed CEGCN is both qualitatively and quantitatively competitive compared with other state-of-the-art methods. |
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| AbstractList | Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions; hence, the HSI spatial contextual structure can be better modeled. However, to reduce the computational complexity and promote the semantic structure learning of land covers, GCN usually works on superpixel-based nodes rather than pixel-based nodes; thus, the pixel-level spectral–spatial features cannot be captured. To fully leverage the advantages of the CNN and GCN, we propose a heterogeneous deep network called CNN-enhanced GCN (CEGCN), in which CNN and GCN branches perform feature learning on small-scale regular regions and large-scale irregular regions, and generate complementary spectral–spatial features at pixel and superpixel levels, respectively. To alleviate the structural incompatibility of the data representation between the Euclidean data-oriented CNN and non-Euclidean data-oriented GCN, we propose the graph encoder and decoder to propagate features between image pixels and graph nodes, thus enabling the CNN and GCN to collaborate in a single network. In contrast to other GCN-based methods that encode HSI into a graph during preprocessing, we integrate the graph encoding process into the network and learn edge weights from training data, which can promote the node feature learning and make the graph more adaptive to HSI content. Extensive experiments on three data sets demonstrate that the proposed CEGCN is both qualitatively and quantitatively competitive compared with other state-of-the-art methods. |
| Author | Xiao, Liang Yang, Jingxiang Liu, Qichao Wei, Zhihui |
| Author_xml | – sequence: 1 givenname: Qichao orcidid: 0000-0003-0134-9450 surname: Liu fullname: Liu, Qichao email: qc.l@njust.edu.cn organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 2 givenname: Liang orcidid: 0000-0003-0178-9384 surname: Xiao fullname: Xiao, Liang email: xiaoliang@mail.njust.edu.cn organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 3 givenname: Jingxiang orcidid: 0000-0002-1234-0614 surname: Yang fullname: Yang, Jingxiang email: yang123jx@njust.edu.cn organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China – sequence: 4 givenname: Zhihui orcidid: 0000-0002-4841-6051 surname: Wei fullname: Wei, Zhihui email: gswei@njust.edu.cn organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China |
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| CODEN | IGRSD2 |
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| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E RIA RIE AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M |
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| SubjectTerms | Artificial neural networks Classification Coders Computational modeling Computer applications Convolution Convolutional neural network (CNN) Data Data processing Decoding Deep learning Feature extraction feature fusion graph convolutional network (GCN) graph encoder and decoder hyperspectral image (HSI) classification Hyperspectral imaging Image classification Incompatibility Kernel Learning Machine learning Methods Neural networks Nodes Pixels Regions Training Training data |
| Title | CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification |
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