A machine learning approach to hyperspectral detection of solid targets

Amanda Ziemann; Michal Kucer; James Theiler

doi:10.1117/12.2305232

8 May 2018 A machine learning approach to hyperspectral detection of solid targets

Amanda Ziemann, Michal Kucer, James Theiler

Proceedings Volume 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV; 1064404 (2018) https://doi.org/10.1117/12.2305232
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

We describe and compare two approaches to solid subpixel target detection in hyperspectral imagery. The first approach requires explicit models for both the target and the background, and employs a generalized likelihood ratio in order to obtain a detector that is optimized to those specific models. When this approach is most successful, a closed-form solution is obtained that permits the detector to be efficiently applied. A specific example of this approach is outlined in some detail, leading to the elliptically-contoured finite-target matched filter (EC-FTMF), a variant of the classical FTMF algorithm that uses a multivariate t-distribution instead of a Gaussian as the model for the background. The second approach also requires an explicit model of the target, but does not need a model for the background. In this second approach, matched pairs of data samples are created: for each pixel in the original hyperspectral image, a corresponding pixel is generated by implanting the target into the original pixel. These matched pairs are used as training data for a machine learning algorithm to classify pixels as either non-target or target. Here we use a support vector machine, but the matched pair machine learning (MPML) framework does not restrict the choice of classifier type. Detectors using both approaches are applied both to simulated data (with Gaussian and with multivariate fit distributed backgrounds) and to real hyperspectral data with known, referenced targets.

Citation Download Citation

Amanda Ziemann, Michal Kucer, and James Theiler "A machine learning approach to hyperspectral detection of solid targets", Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064404 (8 May 2018); https://doi.org/10.1117/12.2305232

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