Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 18, Issue 7, Pages 1279-1283Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.2994745
Keywords
Anomaly detection; Hyperspectral imaging; Dictionaries; Sparse matrices; Matrix decomposition; Object detection; Anomaly detection; dictionary construction; local outlier factor (LOF); low-rank representation (LRR); matched filter
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Funding
- National Nature Science Foundation of China [61671408]
- Ministry of Education of China [6141A02022350, 6141A02022362]
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This letter proposes a novel method for hyperspectral anomaly detection based on the LRR model, which improves detection performance by enhancing the dictionary and using an adaptive filter based on LOF.
In recent years, low-rank representation (LRR) has attracted considerable attention in the field of hyperspectral anomaly detection. The main objective of LRR-based methods is to extract anomalies from the complex background. However, the presence of anomalies in the background dictionary can lower the detection performance. In this letter, a novel method is proposed for hyperspectral anomaly detection based on the LRR model. This method facilitates the discrimination between the anomalous targets and background by utilizing a novel dictionary and an adaptive filter based on the local outlier factor (LOF). In order to exclude the potential anomalies from the dictionary, the ranking of LOF scores for each pixel is adapted to select the potential background pixels as dictionary atoms. A filter that explores the intrinsic spatial structure is designed to enhance the differences between the anomalies and the background pixels. The experimental results that conducted on three real-world data sets demonstrate that the proposed method achieves a better performance than several state-of-the-art hyperspectral anomaly detection methods.
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