Hyperspectral anomaly detection using enhanced global factors

Todd J. Paciencia; Kenneth W. Bauer Jr.

doi:10.1117/12.2223865

12 May 2016 Hyperspectral anomaly detection using enhanced global factors

Todd J. Paciencia, Kenneth W. Bauer Jr.

Proceedings Volume 9844, Automatic Target Recognition XXVI; 98440O (2016) https://doi.org/10.1117/12.2223865
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Dimension reduction techniques have become one popular unsupervised approach used towards detecting anomalies in hyperspectral imagery. Although demonstrating promising results in the literature on specific images, these methods can become difficult to directly interpret and often require tuning of their parameters to achieve high performance on a specific set of images. This lack of generality is also compounded by the need to remove noise and atmospheric absorption spectral bands from the image prior to detection. Without a process for this band selection and to make the methods adaptable to different image compositions, performance becomes difficult to maintain across a wider variety of images. Here, we present a framework that uses factor analysis to provide a robust band selection and more meaningful dimension reduction with which to detect anomalies in the imagery. Measurable characteristics of the image are used to create an automated decision process that allows the algorithm to adjust to a particular image, while maintaining high detection performance. The framework and its algorithms are detailed, and results are shown for forest, desert, sea, rural, urban, anomaly-sparse, and anomaly-dense imagery types from different sensors. Additionally, the method is compared to current state-of-the-art methods and is shown to be computationally efficient.

Citation Download Citation

Todd J. Paciencia and Kenneth W. Bauer Jr. "Hyperspectral anomaly detection using enhanced global factors", Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440O (12 May 2016); https://doi.org/10.1117/12.2223865

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