Abstract
With high spectral resolution, hyperspectral image(HSI) data will result in the Hughes phenomenon, which brings a huge challenge to hyperspectral image classification(HIC). Feature extraction can be applied to address this problem. But several traditional methods often ignore the spatial structure information of HSI data. In this paper, we propose a tensor nuclear norm based matrix regression based projections(TNMRP) for feature extraction of hyperspectral images. Firstly, TNMRP preprocesses the data by a filling method. Then, it automatically builds the graph of block-tensor samples and uses the optimal sparse coding coefficients to obtain the weight matrix. Finally, based on tensor representation, TNMRP calculates the optimal projection matrix. Experiments of classification on Indian Pines and Pavia University databases demonstrate the effectiveness of our proposed method.
Original language | English |
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Title of host publication | Proceedings of the 2023 26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023 |
Number of pages | 6 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Publication date | 2023 |
Pages | 1037-1042 |
ISBN (Electronic) | 9798350331684 |
DOIs | |
Publication status | Published - 2023 |
Event | 26th International Conference on Computer Supported Cooperative Work in Design - Rio de Janeiro, Brazil Duration: 24 May 2023 → 26 May 2023 Conference number: 26 |
Conference
Conference | 26th International Conference on Computer Supported Cooperative Work in Design |
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Number | 26 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 24/05/2023 → 26/05/2023 |
Sponsor | IEEE, Kunming University, Université de technologie de Compiègne, Yunnan University, Zhejiang University |
Series | Proceedings of the 2023 26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023 |
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Keywords
- Classification
- Feature Extraction
- Hyperspectral Image(HSI)
- NMRP
- Tensor
- TNMRP