Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation
Baozhi Cheng, Yan Gao · 2024
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Description
Baozhi Cheng and Yan Gao wrote this research paper, published in 2024. It presents a method for spotting anomalies in hyperspectral images, pictures that record hundreds of narrow color bands, where odd targets hide in a busy background.
Classic methods split the image into a smooth background part and a sparse oddity part, but noise and clutter trip them up. The authors first cluster the image into subspaces, cutting redundancy between bands. A fractional Fourier transform then sharpens the split between background and target while damping noise. The classic decomposition follows, and a tuned detector flags the anomalies in each subspace. Combined results across five real datasets scored near the top of the standard accuracy curve. The method aims at uses from defense scanning to crop and mineral surveys.
Catalog record
| CREATOR | Baozhi Cheng, Yan Gao |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |