Improved outlier identification in hyperspectral imaging via nonlinear dimensionality reduction

C. C. Olson; J. M. Nichols; J. V. Michalowicz; F. Bucholtz

doi:10.1117/12.851811

12 May 2010 Improved outlier identification in hyperspectral imaging via nonlinear dimensionality reduction

C. C. Olson, J. M. Nichols, J. V. Michalowicz, F. Bucholtz

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 769507 (2010) https://doi.org/10.1117/12.851811
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

We use a nonlinear dimensionality reduction technique to improve anomaly detection in a hyperspectral imaging application. A nonlinear transformation, diffusion map, is used to map pixels from the high-dimensional spectral space to a (possibly) lower-dimensional manifold. The transformation is designed to retain a measure of distance between the selected pixels. This lower-dimensional manifold represents the background of the scene with high probability and selecting a subset of points reduces the computational overhead associated with diffusion map. The remaining pixels are mapped to the manifold by means of a Nystr¨om extension. A distance measure is computed for each new pixel and those that do not reside near the background manifold, as determined by a threshold, are identified as anomalous. We compare our results with the RX and subspace RX methods of anomaly detection.

Citation Download Citation

C. C. Olson, J. M. Nichols, J. V. Michalowicz, and F. Bucholtz "Improved outlier identification in hyperspectral imaging via nonlinear dimensionality reduction", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769507 (12 May 2010); https://doi.org/10.1117/12.851811

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