Function approximation framework for region of interest determination in synthetic aperture radar images

Ravi Kothari; David Ensley

doi:10.1117/1.601827

1 October 1998 Function approximation framework for region of interest determination in synthetic aperture radar images

Ravi Kothari, David Ensley

Optical Engineering, Vol. 37, Issue 10, (October 1998). https://doi.org/10.1117/1.601827

Region of interest (ROI) determination is a first and crucial step performed in an automatic target recognition (ATR) system. The goal of ROI determination is to identify candidate regions that may have potential targets. To be most effective, this initial detection (or focus of attention) stage must reject clutter (noise or countermeasures that provide target like characteristics), while ensuring that regions with true targets are not missed. We present a novel approach to ROI determination in synthetic aperture radar (SAR) images for ATR based on the premise that regions with targets would require a model with more free parameters to smoothly approximate the magnitude of the return. Toward that end, we use a sigmoidal multilayered feed-forward neural network with selected lateral connections between hidden layer neurons to approximate the return in disjoint square patches of the SAR image. This network probably uses as few neurons as possible to produce a desired approximation and thus enables the determination of the number of parameters used in approximating the return in an image patch. Those squares of the image that require a large number of neurons (more free parameters) are then labeled as ROIs. Results obtained with synthetic and real-world SAR images are used to demonstrate the effectiveness of the proposed method. A significant advantage of the proposed method is that it does not require the presence of a training data set, which, given the variability in SAR images and target signatures, is difficult to obtain.

Citation Download Citation

Ravi Kothari and David Ensley "Function approximation framework for region of interest determination in synthetic aperture radar images," Optical Engineering 37(10), (1 October 1998). https://doi.org/10.1117/1.601827

Published: 1 October 1998

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

Members: $24.00

Non-members: $28.00 ADD TO CART

JOURNAL ARTICLE
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Neurons

Synthetic aperture radar

Neural networks

Automatic target recognition

Data modeling

Optical engineering

Target detection

Show All Keywords

Keywords/Phrases

Search In:

Publication Years