3 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
Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping
Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder–decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones