SAR image despeckling through convolutional neural networks
By Giovanni Chierchia, Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva. IEEE International Geoscience and Remote Sensing Symposium, 2017
297 citations recorded by the source corpus.
In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/69884aa620034bc4d54f0788ef3ce36faa5b8509
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
In Computer Vision and Pattern Recognition
- Vishal M. Patel
- He Zhang
- Lichao Mou
- Xiaoxiang Zhu
- Puyang Wang
- Richard Bamler
- Mohsin Ali
- Yuanyuan Wang
Citations in the corpus
- Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “7: The comparison of speckle reduction among SAR-BM3D [119], SAR-CNN [52], and CNN-NLM applied to a small strip of COSMO-SkyMed data over Caserta, Italy, where the reference clean image has been obtained by temporal multi-looking applied to a stack of SAR images [120].”
- SAR Image Despeckling Using a Convolutional Neural Network (2017, methodology): “Another fundamental difference between [20] and our work is that the output of [20] is the log noise, whereas ours is the denoised image.”