Self-Adaptive Low-Rank and Sparse Decomposition for Hyperspectral Anomaly Detection
Hyperspectral anomaly detection is a widely used technique for exploring target of interest in hyperspectral images (HSIs). In recent years, the low-rank and sparse-decomposition-based anomaly detection model has attracted extensive attention. However, these models suffer from two main problems. Fir...
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| 主要な著者: | , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
IEEE
2022-01-01
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| シリーズ: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| 主題: | |
| オンライン･アクセス: | https://ieeexplore.ieee.org/document/9767636/ |
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