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Article summary:
1. Anomaly detection in hyperspectral images (HSIs) is an important task in the remote-sensing domain.
2. A tensor-based anomaly detection algorithm is proposed to preserve the spatial-spectral information of the original data.
3. The proposed method is tested on several real hyperspectral datasets and shows superior performance compared to existing methods.
Article analysis:
The article provides a comprehensive overview of the current state of anomaly detection in hyperspectral images, as well as a detailed description of the proposed tensor-based anomaly detection algorithm. The authors provide evidence for their claims by citing relevant literature and providing experimental results from several real hyperspectral datasets. The article does not appear to be biased or one-sided, as it presents both sides of the argument equally and fairly. Furthermore, all potential risks are noted and discussed in detail, which adds to its trustworthiness and reliability. However, there are some missing points of consideration that could have been explored further, such as how the proposed method can be applied to other types of data sets or how it can be improved upon in future research. Additionally, more evidence could have been provided for some of the claims made in order to strengthen their validity. All in all, this article appears to be trustworthy and reliable overall, with only minor areas for improvement.