Abstract
SAR image interpretation is always impaired by speckle that is a multiplicative noise due to interference among the backscatterings from targets inside a resolution cell. Many algorithms for both single and multi-channel SAR despeckling have been proposed in the last forty years following different approaches. Recently, a multi-objective convolutional neural network, named MONet, has been proposed for single channel SAR despeckling. It relies on a mulit-objectvie cost function that takes into account three main aspects of the SAR images: noise removal, details and statistics preservation. Inspired by MONet, in this paper a deep learning method for InSAR phase filtering is proposed. The idea is to benefit from the multi-objective cost function defined in MONet that seems to perfectly fit with the interferogram denoising. This is the first step towards a solution able to provide a complete processed multi-channel product.
Original language | English |
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Title of host publication | IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 419-422 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-6654-0369-6 |
ISBN (Print) | 978-1-6654-4762-1, 978-1-6654-0368-9 (USB) |
DOIs | |
Publication status | Published - 12 Oct 2021 |
Event | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021 https://igarss2021.com |
Publication series
Name | IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
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Publisher | IEEE |
Volume | 2021 |
ISSN (Print) | 2153-6996 |
ISSN (Electronic) | 2153-7003 |
Conference
Conference | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 |
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Abbreviated title | IGARSS 2021 |
Country/Territory | Belgium |
City | Brussels |
Period | 12/07/21 → 16/07/21 |
Internet address |
Keywords
- Deep learning
- Denoising
- MultiChannel
- SAR
- 22/1 OA procedure