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- Title
Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary.
- Authors
Cheng, Xi; Mu, Ruiqi; Lin, Sheng; Zhang, Min; Wang, Hai
- Abstract
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.
- Subjects
REPRESENTATIONS of graphs; ENCYCLOPEDIAS &; dictionaries; MATHEMATICAL regularization
- Publication
Remote Sensing, 2024, Vol 16, Issue 11, p1837
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs16111837