AI-Based Performance-Optimized Quantization for Future SAR Systems

Author:

Gollin Nicola1,Martone Michele1,Krieger Gerhard1,Rizzoli Paola1

Affiliation:

1. Microwaves and Radar Institute,German Aerospace Center (DLR)

Publisher

IEEE

Reference7 articles.

1. U-net: Convolutional networks for biomedical image segmentation;ronneberger;Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015 18th International Conference Munich Germany October 5-9 2015 Proceedings Part III 18,2015

2. Azimuth-Switched Quantization for SAR Systems and Performance Analysis on TanDEM-X Data

3. FDBAQ a novel encoding scheme for Sentinel-1

4. Taxi: A versatile processing chain for experimental TanDEM-X product evaluation

5. Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Raw Data Compression Exploiting Model-Based Approaches and Artificial Intelligence For Present And Next-Generation SAR Systems;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Raw SAR Data Compression with Deep Learning;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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