Adaptive Target Enhancer : Bridging the Gap between Synthetic and Measured SAR Images for Automatic Target Recognition

• Automatic target recognition (ATR) algorithms have been successfully used for vehicle classification in synthetic aperture radar (SAR) images over the past few decades. For this application, however, the scarcity of labeled data is often a limiting factor for supervised approaches. While the advent of computer-simulated images may result in additional data for ATR, there is still a substantial gap between synthetic and measured images. In this paper, we propose the so-called adaptive target enhancer (ATE), a tool designed to automatically delimit and weight the region of an image that contains or is affected by the presence of a target. Results for the publicly released Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset show that, by defining regions of interest and suppressing the background, we can increase the classification accuracy from 68% to 84% while only using artificially generated images for training. © 2023 IEEE.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Nyckelord

Adaptive filtering

automatic target recognition (ATR)

MSTAR

SAMPLE

synthetic aperture radar (SAR)

Adaptive filters

Automatic target recognition

Classification (of information)

Image enhancement

Radar imaging

Radar measurement

Radar target recognition

Additional datum

Labeled data

Simulated images

Synthetic and measured paired and labeled experiment

Synthetic aperture radar

Synthetic aperture radar images

Target recognition algorithms

Vehicle classification

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)