Back to Search
Start Over
Despeckling Polarimetric SAR Data Using a Multistream Complex-Valued Fully Convolutional Network
- Source :
- IEEE Geoscience and Remote Sensing Letters 19 (2022), IEEE Geoscience and Remote Sensing Letters, 19, 1-5, IEEE geoscience and remote sensing letters, 19, 1-5. IEEE
- Publication Year :
- 2022
-
Abstract
- A polarimetric synthetic aperture radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterization. PolSAR images are inherently affected by speckle. Therefore, before deriving ad hoc products from the data, the polarimetric covariance matrix needs to be estimated by reducing speckle. In recent years, deep learning-based despeckling methods have started to evolve from single-channel SAR images to PolSAR images. To this aim, deep learning-based approaches separate the real and imaginary components of the complex-valued covariance matrix and use them as independent channels in standard convolutional neural networks (CNNs). However, this approach neglects the mathematical relationship that exists between the real and imaginary components, resulting in suboptimal output. Here, we propose a multistream complex-valued fully convolutional network (FCN) (CV-deSpeckNet1) to reduce speckle and effectively estimate the PolSAR covariance matrix. To evaluate the performance of CV-deSpeckNet, we used Sentinel-1 dual polarimetric SAR images to compare against its real-valued counterpart that separates the real and imaginary parts of the complex covariance matrix. CV-deSpeckNet was also compared against the state of the art PolSAR despeckling methods. The results show that CV-deSpeckNet was able to be trained with a fewer number of samples, has a higher generalization capability, and resulted in higher accuracy than its real-valued counterpart and state-of-the-art PolSAR despeckling methods. These results showcase the potential of complex-valued deep learning for PolSAR despeckling.
- Subjects :
- Synthetic aperture radar
Covariance matrices
Noise measurement
Computer science
Polarimetry
Iterative reconstruction
Convolutional neural network
Scattering
Speckle pattern
Laboratory of Geo-information Science and Remote Sensing
Radar polarimetry
FOS: Electrical engineering, electronic engineering, information engineering
Laboratorium voor Geo-informatiekunde en Remote Sensing
Electrical and Electronic Engineering
speckle
convolutional neural network (CNN)
Covariance matrix
business.industry
Deep learning
Image and Video Processing (eess.IV)
deep learning
Pattern recognition
Electrical Engineering and Systems Science - Image and Video Processing
Geotechnical Engineering and Engineering Geology
PE&RC
ITC-ISI-JOURNAL-ARTICLE
Image reconstruction
polarimetric SAR (PolSAR)
Artificial intelligence
business
Complex-valued
Subjects
Details
- Language :
- English
- ISSN :
- 1545598X
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- IEEE Geoscience and Remote Sensing Letters
- Accession number :
- edsair.doi.dedup.....3785be0c706f327e7e6280dfc31b5177
- Full Text :
- https://doi.org/10.1109/lgrs.2021.3066311