Kernel-machine-based classification in multi-polarimetric SAR data

Wolfgang Middelmann; Alfons Ebert; Ulrich Thoennessen

doi:10.1117/12.603333

19 May 2005 Kernel-machine-based classification in multi-polarimetric SAR data

Wolfgang Middelmann, Alfons Ebert, Ulrich Thoennessen

Proceedings Volume 5808, Algorithms for Synthetic Aperture Radar Imagery XII; (2005) https://doi.org/10.1117/12.603333
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The focus of this paper is the classification of military vehicles in multi-polarimetric high-resolution spotlight SAR images in an ATR framework. Kernel machines as robust classification methods are the basis of our approach. A novel kernel machine the Relevance Vector Machine with integrated Generator (RVMG) controlling the trade-off between classification quality and computational effort is used. It combines the high classification quality of the Support Vector Machine by margin maximization and the low effort of the Relevance Vector Machine caused by the special statistical approach. Moreover multi-class classification capability is given by an efficient decision heuristic, an adaptive feature extraction based on Fourier coefficients allows the module to do real time execution, and a parameterized reject criterion is proposed in this paper. Investigations with a nine class data set from QinetiQ deal with fully polarimetric SAR data. The objective is to assess polarimetric features in combination with several kernel machines. Tests approve the high potential of RVMG. Moreover it is shown that polarimetric features can improve the classification quality for hard targets. Among these the simple energy based features prove more favorable than complex ones. Especially the two coplanar polarizations embody the essential information, but a better generalizability is caused by using all four channels. An important property of a classifier used in the ATR framework is the capability to reject objects not belonging to any of the trained classes. Therefore the QinetiQ data are divided into four training classes and five classes of confusion objects. The classification module with reject criterion is controlled by the reject parameter and the kernel parameter. Both parameters are varied to determine ROC curves related to different polarimetric features.

Citation Download Citation

Wolfgang Middelmann, Alfons Ebert, and Ulrich Thoennessen "Kernel-machine-based classification in multi-polarimetric SAR data", Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); https://doi.org/10.1117/12.603333

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