Image and Signal Processing Methods

Sparsity divergence index based on locally linear embedding for hyperspectral anomaly detection

[+] Author Affiliations
Lili Zhang

Harbin Engineering University, College of Information and Communication Engineering, Room 504, Building 21, No. 145 Nantong Street, Nangang District, Harbin City, Heilongjiang Province 150001, China

Daqing Normal University, College of Mechanical and Electrical Engineering, Room 401, Building of College of Mechanical and Electrical Engineering, No. 85 Xiyuan Street, Ranghulu District, Daqing City, Heilongjiang Province 163712, China

Chunhui Zhao

Harbin Engineering University, College of Information and Communication Engineering, Room 504, Building 21, No. 145 Nantong Street, Nangang District, Harbin City, Heilongjiang Province 150001, China

J. Appl. Remote Sens. 10(2), 025026 (Jun 28, 2016). doi:10.1117/1.JRS.10.025026
History: Received December 23, 2015; Accepted June 3, 2016
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Abstract.  Hyperspectral imagery (HSI) has high spectral and spatial resolutions, which are essential for anomaly detection (AD). Many anomaly detectors assume that the spectrum signature of HSI pixels can be modeled with a Gaussian distribution, which is actually not accurate and often leads to many false alarms. Therefore, a sparsity model without any distribution hypothesis is usually employed. Dimensionality reduction (DR) as a preprocessing step for HSI is important. Principal component analysis as a conventional DR method is a linear projection and cannot exploit the nonlinear properties in hyperspectral data, whereas locally linear embedding (LLE) as a local, nonlinear manifold learning algorithm works well for DR of HSI. A modified algorithm of sparsity divergence index based on locally linear embedding (SDI-LLE) is thus proposed. First, kernel collaborative representation detection is adopted to calculate the sparse dictionary matrix of local reconstruction weights in LLE. Then, SDI is obtained both in the spectral and spatial domains, where spatial SDI is computed after DR by LLE. Finally, joint SDI, combining spectral SDI and spatial SDI, is computed, and the optimal SDI is performed for AD. Experimental results demonstrate that the proposed algorithm significantly improves the performance, when compared with its counterparts.

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Citation

Lili Zhang and Chunhui Zhao
"Sparsity divergence index based on locally linear embedding for hyperspectral anomaly detection", J. Appl. Remote Sens. 10(2), 025026 (Jun 28, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.025026


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