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Journal of Applied Remote Sensing

Sparsity divergence index based on locally linear embedding for hyperspectral anomaly detection
Author(s): Lili Zhang; Chunhui Zhao
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Paper 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.

Paper Details

Date Published: 28 June 2016
PDF: 24 pages
J. Appl. Remote Sens. 10(2) 025026 doi: 10.1117/1.JRS.10.025026
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Lili Zhang, Harbin Engineering Univ. (China)
Daqing Normal Univ. (China)
Chunhui Zhao, Harbin Engineering Univ. (China)


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