| Autor: |
Xi Cheng, Ruiqi Mu, Sheng Lin, Min Zhang, Hai Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Remote Sensing, Vol 16, Iss 11, p 1837 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs16111837 |
| Popis: |
In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to detect anomalies. To address this challenge, a novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection. To be specific, dual graph regularization, which combines spectral and spatial regularization, provides a new paradigm for LRR, and it can effectively preserve the local geometrical structure in the spectral and spatial information. To obtain a robust background dictionary, a novel adaptive dictionary strategy is utilized for the LRR model. In addition, extensive comparative experiments and an ablation study were conducted to demonstrate the superiority and practicality of the proposed DGRAD-LRR method. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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