Kernel-based feature extraction and its application on HRR signatures

Honglin Li; Yi Zhao; Junshui Ma; Stanley C. Ahalt

doi:10.1117/12.477030

25 July 2002 Kernel-based feature extraction and its application on HRR signatures

Honglin Li, Yi Zhao, Junshui Ma, Stanley C. Ahalt

Proceedings Volume 4726, Automatic Target Recognition XII; (2002) https://doi.org/10.1117/12.477030
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

Kernel-based Feature Extraction (KFE) is an emerging nonlinear discriminant feature extraction technique. In many classification scenarios using KFE allows the dimensionality of raw data to be reduced while class separability is preserved or even improved. KFE offers better performance than alternative linear algorithms because it employs nonlinear discriminating information among the classes. In this paper, we explore the potential application of KFE to radar signatures, as might be used for Automatic Target Recognition (ATR). Radar signatures can be problematic for many traditional ATR algorithms because of their unique characteristics. For example, some unprocessed radar signatures are high dimensional, linearly inseparable, and extremely sensitive to aspect changes. Applying KFE on High Range Resolution (HRR) radar signatures, we observe that KFE is quite effective on HRR data in terms of preserving/improving separability and reducing the dimensionality of the original data. Furthermore, our experiments indicate the number of extracted features that are needed for HRR radar signatures.

Citation Download Citation

Honglin Li, Yi Zhao, Junshui Ma, and Stanley C. Ahalt "Kernel-based feature extraction and its application on HRR signatures", Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); https://doi.org/10.1117/12.477030

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