285 research outputs found
Non-Local Compressive Sensing Based SAR Tomography
Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse
reconstruction problem and, hence, can be solved using compressive sensing (CS)
algorithms. This paper proposes solutions for two notorious problems in this
field: 1) TomoSAR requires a high number of data sets, which makes the
technique expensive. However, it can be shown that the number of acquisitions
and the signal-to-noise ratio (SNR) can be traded off against each other,
because it is asymptotically only the product of the number of acquisitions and
SNR that determines the reconstruction quality. We propose to increase SNR by
integrating non-local estimation into the inversion and show that a reasonable
reconstruction of buildings from only seven interferograms is feasible. 2)
CS-based inversion is computationally expensive and therefore barely suitable
for large-scale applications. We introduce a new fast and accurate algorithm
for solving the non-local L1-L2-minimization problem, central to CS-based
reconstruction algorithms. The applicability of the algorithm is demonstrated
using simulated data and TerraSAR-X high-resolution spotlight images over an
area in Munich, Germany.Comment: 10 page
Diffusion Models for Interferometric Satellite Aperture Radar
Probabilistic Diffusion Models (PDMs) have recently emerged as a very
promising class of generative models, achieving high performance in natural
image generation. However, their performance relative to non-natural images,
like radar-based satellite data, remains largely unknown. Generating large
amounts of synthetic (and especially labelled) satellite data is crucial to
implement deep-learning approaches for the processing and analysis of
(interferometric) satellite aperture radar data. Here, we leverage PDMs to
generate several radar-based satellite image datasets. We show that PDMs
succeed in generating images with complex and realistic structures, but that
sampling time remains an issue. Indeed, accelerated sampling strategies, which
work well on simple image datasets like MNIST, fail on our radar datasets. We
provide a simple and versatile open-source
https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and
evaluate PDMs using any dataset on a single GPU
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
A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage
An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double-l1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes
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
A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR
regularization is used for finding sparse solutions to an
underdetermined linear system. As sparse signals are widely expected in remote
sensing, this type of regularization scheme and its extensions have been widely
employed in many remote sensing problems, such as image fusion, target
detection, image super-resolution, and others and have led to promising
results. However, solving such sparse reconstruction problems is
computationally expensive and has limitations in its practical use. In this
paper, we proposed a novel efficient algorithm for solving the complex-valued
regularized least squares problem. Taking the high-dimensional
tomographic synthetic aperture radar (TomoSAR) as a practical example, we
carried out extensive experiments, both with simulation data and real data, to
demonstrate that the proposed approach can retain the accuracy of second order
methods while dramatically speeding up the processing by one or two orders.
Although we have chosen TomoSAR as the example, the proposed method can be
generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin
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