A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

By Fabrizio Argenti, Alessandro Lapini, Tiziano Bianchi, Luciano Alparone. IEEE Geoscience and Remote Sensing Magazine, 2013

516 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/4cdf203b23fa5c1330ff38084ba36d220138b55e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Richard Bamler
2. Xiaoxiang Zhu
3. Puyang Wang
4. Giovanni Poggi
5. Luisa Verdoliva
6. Vishal M. Patel
7. Feng Xu
8. Davide Cozzolino

In Computer Vision and Pattern Recognition

1. Luisa Verdoliva
2. Davide Cozzolino
3. Vishal M. Patel
4. He Zhang
5. Giovanni Poggi
6. Lichao Mou
7. Xiaoxiang Zhu
8. Puyang Wang

In Media Technology

1. Luisa Verdoliva
2. Davide Cozzolino
3. Giovanni Poggi
4. Xiaoxiang Zhu
5. Lichao Mou
6. Puyang Wang
7. Richard Bamler
8. Vishal M. Patel

Citations in the corpus

• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, background): “For a full overview of these techniques, the reader is referred to [129].”
• SAR Image Despeckling Using a Convolutional Neural Network (2017, methodology): “Furthermore, due to local processing nature of some of these methods, they often fail to preserve sharp features such as edges and often contain block artifacts in the denoised image [11], [14].”
• SAR image despeckling through convolutional neural networks (2017, background): “A number of approaches have been proposed in the last few years to suppress speckle while preserving relevant image features [1].”