Hyperspectral Anomaly Detection by the Use of Background Joint Sparse Representation

In this paper, we propose a hyperspectral image anomaly detection model by the use of background joint sparse representation (BJSR). With a practical binary hypothesis test model, the proposed approach consists of the following steps. The adaptive orthogonal background complementary subspace is firs...

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Bibliographic Details
Published inIEEE journal of selected topics in applied earth observations and remote sensing Vol. 8; no. 6; pp. 2523 - 2533
Main Authors Li, Jiayi, Zhang, Hongyan, Zhang, Liangpei, Ma, Li
Format Journal Article
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Anomaly detection (AD)
Detectors
Dictionaries
Estimation
hyperspectral imagery
Hyperspectral imaging
joint sparse representation (JSR)
Joints
Noise
robust background estimation
Anomaly detection (AD)
joint sparse representation (JSR)
robust background estimation
hyperspectral imagery
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ISSN1939-1404
2151-1535
DOI10.1109/JSTARS.2015.2437073

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Summary:In this paper, we propose a hyperspectral image anomaly detection model by the use of background joint sparse representation (BJSR). With a practical binary hypothesis test model, the proposed approach consists of the following steps. The adaptive orthogonal background complementary subspace is first estimated by the BJSR, which adaptively selects the most representative background bases for the local region. An unsupervised adaptive subspace detection method is then proposed to suppress the background and simultaneously highlight the anomaly component. The experimental results confirm that the proposed algorithm obtains a desirable detection performance and outperforms the classical RX-based anomaly detectors and the orthogonal subspace projection-based detectors.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2015.2437073