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Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction

Huyan, Ning ; Zhang, Xiangrong ; Zhou, Huiyu ; Jiao, Licheng

IEEE transactions on geoscience and remote sensing, 2019-04, Vol.57 (4), p.2263-2276 [Periódico revisado por pares]

New York: IEEE

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Título:
Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction
Autor: Huyan, Ning ; Zhang, Xiangrong ; Zhou, Huiyu ; Jiao, Licheng
Assuntos: Anomalies ; Anomaly detection ; background dictionary ; Background noise ; Constraint modelling ; Construction ; Decomposition ; Detection ; Dictionaries ; Glossaries ; Homogeneity ; hyperspectral images (HSIs) ; Hyperspectral imaging ; Image detection ; joint sparse representation (JSR) ; low rank ; Matrix decomposition ; Methods ; Noise ; Object detection ; potential anomaly dictionary ; Principal component analysis
É parte de: IEEE transactions on geoscience and remote sensing, 2019-04, Vol.57 (4), p.2263-2276
Descrição: In this paper, we propose a new anomaly detection method for hyperspectral images based on two well-designed dictionaries: background dictionary and potential anomaly dictionary. In order to effectively detect an anomaly and eliminate the influence of noise, the original image is decomposed into three components: background, anomalies, and noise. In this way, the anomaly detection task is regarded as a problem of matrix decomposition. Considering the homogeneity of background and the sparsity of anomalies, the low-rank and sparse constraints are imposed in our model. Then, the background and potential anomaly dictionaries are constructed using the background and anomaly priors. For the background dictionary, a joint sparse representation (JSR)-based dictionary selection strategy is proposed, assuming that the frequently used atoms in the overcomplete dictionary tend to be the background. In order to make full use of the prior information of anomalies hidden in the scene, the potential anomaly dictionary is constructed. We define a criterion, i.e., the anomalous level of a pixel, by using the residual calculated in the JSR model within its local region. Then, it is combined with a weighted term to alleviate the influence of noise and background. Experiments show that our proposed anomaly detection method based on potential anomaly and background dictionaries construction can achieve superior results compared with other state-of-the-art methods.
Editor: New York: IEEE
Idioma: Inglês

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Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction

Huyan, Ning ; Zhang, Xiangrong ; Zhou, Huiyu ; Jiao, Licheng

New York: IEEE

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