Hyperspectral anomaly detection based on variable dimension for clutter subspace

Edisanter Lo

doi:10.1117/12.920835

24 May 2012 Hyperspectral anomaly detection based on variable dimension for clutter subspace

Edisanter Lo

Proceedings Volume 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII; 839004 (2012) https://doi.org/10.1117/12.920835
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Anomaly detectors based on subspace models have the dimension of the clutter subspace as the parameter with a large range of values. An anomaly detector that has a different parameter with fewer values is proposed. The known pixel from a hyperspectral image is predicted with a linear transformation of the unknown variables from the clutter subspace and the coefficients of the linear transformation are unknown. The dimension of the clutter subspace can vary from one spectral component of the pixel to another. The anomaly detector is the Mahalanobis distance of the error. The experimental results show that the parameter in the anomaly detector has a significantly reduced number of possible values in comparison with the conventional anomaly detectors.

Citation Download Citation

Edisanter Lo "Hyperspectral anomaly detection based on variable dimension for clutter subspace", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839004 (24 May 2012); https://doi.org/10.1117/12.920835

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