Summary of Hyperspectral Anomaly Detection with Self-supervised Anomaly Prior, by Yidan Liu and Weiying Xie and Kai Jiang and Jiaqing Zhang and Yunsong Li and Leyuan Fang
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
by Yidan Liu, Weiying Xie, Kai Jiang, Jiaqing Zhang, Yunsong Li, Leyuan Fang
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for hyperspectral anomaly detection (HAD) by redefining the optimization criterion in the low-rank representation (LRR) model. The authors introduce a self-supervised network, called self-supervised anomaly prior (SAP), which learns characteristics of hyperspectral anomalies through a pretext task. This task involves distinguishing between original hyperspectral images and pseudo-anomaly images generated from the original data. The proposed method also incorporates a dual-purified strategy to provide a refined background representation, enabling better separation of anomalies from complex backgrounds. Experimental results on various datasets demonstrate that the SAP approach outperforms other advanced HAD methods in terms of accuracy and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to find unusual patterns in hyperspectral images using a computer program. The method uses a special kind of learning called self-supervised learning, where the program learns from examples of anomalies and normal data. This helps the program recognize patterns it hasn’t seen before. The authors also improve the background model by combining information from different parts of the image. They test their approach on several datasets and show that it works better than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Optimization » Self supervised
