AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network
Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the lab...
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ftmdpi:oai:mdpi.com:/1424-8220/19/24/5479/ 2023-08-20T04:01:49+02:00 AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network Maryam Rahnemoonfar Jimmy Johnson John Paden 2019-12-12 application/pdf https://doi.org/10.3390/s19245479 EN eng Multidisciplinary Digital Publishing Institute Intelligent Sensors https://dx.doi.org/10.3390/s19245479 https://creativecommons.org/licenses/by/4.0/ Sensors; Volume 19; Issue 24; Pages: 5479 convolutional neural network generative adversarial network ice tracking radar imagery Text 2019 ftmdpi https://doi.org/10.3390/s19245479 2023-07-31T22:53:28Z Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network. Text Antarc* Antarctic Arctic MDPI Open Access Publishing Antarctic Arctic Sensors 19 24 5479 |
institution |
Open Polar |
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MDPI Open Access Publishing |
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language |
English |
topic |
convolutional neural network generative adversarial network ice tracking radar imagery |
spellingShingle |
convolutional neural network generative adversarial network ice tracking radar imagery Maryam Rahnemoonfar Jimmy Johnson John Paden AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network |
topic_facet |
convolutional neural network generative adversarial network ice tracking radar imagery |
description |
Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network. |
format |
Text |
author |
Maryam Rahnemoonfar Jimmy Johnson John Paden |
author_facet |
Maryam Rahnemoonfar Jimmy Johnson John Paden |
author_sort |
Maryam Rahnemoonfar |
title |
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network |
title_short |
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network |
title_full |
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network |
title_fullStr |
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network |
title_full_unstemmed |
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network |
title_sort |
ai radar sensor: creating radar depth sounder images based on generative adversarial network |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/s19245479 |
geographic |
Antarctic Arctic |
geographic_facet |
Antarctic Arctic |
genre |
Antarc* Antarctic Arctic |
genre_facet |
Antarc* Antarctic Arctic |
op_source |
Sensors; Volume 19; Issue 24; Pages: 5479 |
op_relation |
Intelligent Sensors https://dx.doi.org/10.3390/s19245479 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/s19245479 |
container_title |
Sensors |
container_volume |
19 |
container_issue |
24 |
container_start_page |
5479 |
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1774725028813733888 |