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Adaptive Target Enh...
Adaptive Target Enhancer : Bridging the Gap between Synthetic and Measured SAR Images for Automatic Target Recognition
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- Campos, Alexandre B. (författare)
- Microwaves and Radar Institute, German Aerospace Center (DLR), Germany
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- Molin, Ricardo D. (författare)
- Microwaves and Radar Institute, German Aerospace Center (DLR), Germany
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- Ramos, Lucas P. (författare)
- Aeronautics Institute of Technology (ITA), Brazil
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- MacHado, Renato (författare)
- Aeronautics Institute of Technology (ITA), Brazil
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- Vu, Viet Thuy, 1977- (författare)
- Blekinge Tekniska Högskola,Institutionen för matematik och naturvetenskap
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- Pettersson, Mats, 1966- (författare)
- Blekinge Tekniska Högskola,Institutionen för matematik och naturvetenskap
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- Engelska.
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Ingår i: Proceedings of the IEEE Radar Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665436694
- Relaterad länk:
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https://bth.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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)
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