A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery
This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for hyperspectral imagery (HSI). Improved from robust principal component analysis, the RSLAD assumes that the background matrix is low-rank, and the anomaly matrix is sparse with a small portion of nonzero columns (i....
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| 主要な著者: | , , , , |
|---|---|
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI AG
2018-03-01
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| シリーズ: | Remote Sensing |
| 主題: | |
| オンライン･アクセス: | http://www.mdpi.com/2072-4292/10/3/417 |
| タグ: |
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