Convolutional deep learning network for InSAR phase denoising and unwrapping
Asma Fejjari,
Gianluca Valentino,
Johann A. Briffa
et al.
Abstract:Interferometric phase unwrapping is one of the most challenging research topics for the remote sensing community. Recovering and correctly estimating the true interferometric phase signal from the received wrapped one provides critical information about changes in the Earth’s surface over time. Interferometric synthetic aperture radar (InSAR) has been widely used to extract such displacement estimates. However, InSAR images are affected often by a particular type of noise known as Gaussian. The presence of Gau… Show more
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