Probabilistic anomaly detector for remotely sensed hyperspectral data

Lianru Gao; Qiandong Guo; Antonio J. Plaza; Jun Li; Bing Zhang

doi:10.1117/1.JRS.8.083538

3 November 2014 Probabilistic anomaly detector for remotely sensed hyperspectral data

Lianru Gao, Qiandong Guo, Antonio J. Plaza, Jun Li, Bing Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083538 (November 2014). https://doi.org/10.1117/1.JRS.8.083538

Abstract

Anomaly detection is an important technique for remotely sensed hyperspectral data exploitation. In the last decades, several algorithms have been developed for detecting anomalies in hyperspectral images. The Reed-Xiaoli detector (RXD) is one of the most widely used approaches for this purpose. Since the RXD assumes that the distribution of the background is Gaussian, it generally suffers from a high false alarm rate. In order to address this issue, we introduce an unsupervised probabilistic anomaly detector (PAD) based on estimating the difference between the probabilities of the anomalies and the background. The proposed PAD takes advantage of the results provided by the RXD to estimate statistical information for the targets and background, respectively, and then uses an automatic strategy to find the most suitable threshold for the separation of targets from the background. The proposed technique is validated using a synthetic data set and two real hyperspectral data sets with ground-truth information. Our experimental results indicate that the proposed method achieves good detection ratios with adequate computational complexity as compared with other widely used anomaly detectors.

Citation Download Citation

Lianru Gao, Qiandong Guo, Antonio J. Plaza, Jun Li, and Bing Zhang "Probabilistic anomaly detector for remotely sensed hyperspectral data," Journal of Applied Remote Sensing 8(1), 083538 (3 November 2014). https://doi.org/10.1117/1.JRS.8.083538

Published: 3 November 2014

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