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- W4226075372 abstract "Hyperspectral anomaly detection is aimed at detecting observations that differ from their surroundings. To achieve this goal, low-rank models and autoencoders (AEs) have attracted a lot of attention. Although the low-rank model is self-explainable, a low-rank prior may not completely match real data. In contrast, AEs can automatically learn the discriminative features between anomalies and background, whereas AEs are not self-explainable. In this article, a deep low-rank prior-based method (DeepLR) is proposed, which combines a model-driven low-rank prior and a data-driven AE. To be specific, the low-rank prior and a fully convolutional AE architecture are incorporated through modeling an energy minimization problem solved by an iterative optimization framework, in which low-rank background estimation and network training serve as two subproblems. The low-rank background is input into the network to calculate a low-rank regularized loss, constraining the training of the network. Finally, the background can be approximately reconstructed, while the anomalies are reconstructed with significant reconstruction errors; thus, the reconstruction errors indicate the anomalous degree. The experimental results obtained on several public datasets and two large unmanned aerial vehicle (UAV)-borne datasets confirm the merit and viability of the proposed method." @default.
- W4226075372 created "2022-05-05" @default.
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- W4226075372 date "2022-01-01" @default.
- W4226075372 modified "2023-10-15" @default.
- W4226075372 title "Deep Low-Rank Prior for Hyperspectral Anomaly Detection" @default.
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- W4226075372 doi "https://doi.org/10.1109/tgrs.2022.3165833" @default.
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