Local density-based anomaly detection in hyperspectral image

Chen Lou; Huijie Zhao

doi:10.1117/1.JRS.9.095070

15 July 2015 Local density-based anomaly detection in hyperspectral image

Chen Lou, Huijie Zhao

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 095070 (July 2015). https://doi.org/10.1117/1.JRS.9.095070

Abstract

A local density-based anomaly detection (LDAD) method is proposed. LDAD is a nonparameter model-based method, which utilizes the pixel’s local density in hyperspectral images as a criterion to determine the pixel’s anomalousness. In this method, the local density is calculated as a function of the spectral distance between pixels. Distinct from the statistical-based method, there are no assumptions made on the background distributions. Due to the pairwise distance calculation between pixels, LDAD’s computational complexity is quadratic to the total number of pixels. To improve the efficiency, an optimization strategy by pruning is implemented to reduce the unnecessary computational costs. Experiments on real hyperspectral image suggest that the proposed anomaly detector can achieve better detection performance than its counterparts, while keeping the computational cost relatively low by applying the optimization.

Citation Download Citation

Chen Lou and Huijie Zhao "Local density-based anomaly detection in hyperspectral image," Journal of Applied Remote Sensing 9(1), 095070 (15 July 2015). https://doi.org/10.1117/1.JRS.9.095070

Published: 15 July 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available