2 research outputs found
An Unsupervised Generative Neural Approach for InSAR Phase Filtering and Coherence Estimation
Phase filtering and pixel quality (coherence) estimation is critical in
producing Digital Elevation Models (DEMs) from Interferometric Synthetic
Aperture Radar (InSAR) images, as it removes spatial inconsistencies (residues)
and immensely improves the subsequent unwrapping. Large amount of InSAR data
facilitates Wide Area Monitoring (WAM) over geographical regions. Advances in
parallel computing have accelerated Convolutional Neural Networks (CNNs),
giving them advantages over human performance on visual pattern recognition,
which makes CNNs a good choice for WAM. Nevertheless, this research is largely
unexplored. We thus propose "GenInSAR", a CNN-based generative model for joint
phase filtering and coherence estimation, that directly learns the InSAR data
distribution. GenInSAR's unsupervised training on satellite and simulated noisy
InSAR images outperforms other five related methods in total residue reduction
(over 16.5% better on average) with less over-smoothing/artefacts around branch
cuts. GenInSAR's Phase, and Coherence Root-Mean-Squared-Error and Phase Cosine
Error have average improvements of 0.54, 0.07, and 0.05 respectively compared
to the related methods.Comment: to be published in a future issue of IEEE Geoscience and Remote
Sensing Letter