RADAR SENSOR SIMULATION WITH GENERATIVE ADVERSARIAL NETWORK

被引：8

作者：

Rahnemoonfar, Maryam ^{[1
]}

Yari, Masoud ^{[1
]}

Paden, John ^{[2
]}

机构：

[1] Univ Maryland Baltimore Cty, Comp Vis & Remote Sensing Lab, Baltimore, MD 21228 USA

[2] Univ Kansas, Ctr Remote Sensing Ice Sheets, Lawrence, KS 66045 USA

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

convolutional neural network; generative adversarial network; ice tracking; radar imagery;

D O I：

10.1109/IGARSS39084.2020.9323676

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. In this research, we evaluated the performance of synthetically generated snow radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. Our experiments show a very good similarity between real and synthetic snow radar images.

引用

页码：7001 / 7004

页数：4

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