| Autor: |
Yanan Jiang, Heng Zhou, Zitong Zhang, Chunlei Zhang, Kai Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 7135-7150 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2023.3298477 |
| Popis: |
Deep learning methods have shown great promise in automatically extracting features from hyperspectral images (HSIs) for classification purposes. Recently, researchers have recognized the importance of high-order feature interactions—capturing relationships between features in different image regions—in extracting discriminative features. Despite their effectiveness, the existing deep learning models for HSI classification often overlook high-order feature interactions, resulting in suboptimal performance. To address this issue, we propose a novel spectral–spatial multiorder interaction network (S$^{2}$MoINet) for HSI classification. The proposed framework can effectively extract highly discriminative features by leveraging correlations between features in different locations, significantly improving the classification accuracy. More specifically, we design a multiorder spectral–spatial interaction block in the framework to extract the high-order and generalized features by leveraging the interaction between spatial and spectral features. Based on experimental results from four public HSI datasets, it has been shown that the proposed S$^{2}$MoINet delivers optimal classification results when compared to other state-of-the-art methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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