Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data

Ground-based synthetic aperture radar (GBSAR) interferometry for deformation monitoring

Ph. D ThesisGround-based synthetic aperture radar (GBSAR), together with interferometry, represents a powerful tool for deformation monitoring. GBSAR has inherent flexibility, allowing data to be collected with adjustable temporal resolutions through either continuous or discontinuous mode. The goal of this research is to develop a framework to effectively utilise GBSAR for deformation monitoring in both modes, with the emphasis on accuracy, robustness, and real-time capability. To achieve this goal, advanced Interferometric SAR (InSAR) processing algorithms have been proposed to address existing issues in conventional interferometry for GBSAR deformation monitoring. The proposed interferometric algorithms include a new non-local method for the accurate estimation of coherence and interferometric phase, a new approach to selecting coherent pixels with the aim of maximising the density of selected pixels and optimizing the reliability of time series analysis, and a rigorous model for the correction of atmospheric and repositioning errors. On the basis of these algorithms, two complete interferometric processing chains have been developed: one for continuous and the other for discontinuous GBSAR deformation monitoring. The continuous chain is able to process infinite incoming images in real time and extract the evolution of surface movements through temporally coherent pixels. The discontinuous chain integrates additional automatic coregistration of images and correction of repositioning errors between different campaigns. Successful deformation monitoring applications have been completed, including three continuous (a dune, a bridge, and a coastal cliff) and one discontinuous (a hillside), which have demonstrated the feasibility and effectiveness of the presented algorithms and chains for high-accuracy GBSAR interferometric measurement. Significant deformation signals were detected from the three continuous applications and no deformation from the discontinuous. The achieved results are justified quantitatively via a defined precision indicator for the time series estimation and validated qualitatively via a priori knowledge of these observing sites.China Scholarship Council (CSC), Newcastle Universit

An interferometric phase noise reduction method based on modified denoising convolutional neural network

Traditional interferometric synthetic aperture radar (InSAR) denoising methods normally try to estimate the phase fringes directly from the noisy interferogram. Since the statistics of phase noise are more stable than the phase corresponding to complex terrain, it could be easier to estimate the phase noise. In this paper, phase noises rather than phase fringes are estimated first, and then they are subtracted from the noisy interferometric phase for denoising. The denoising convolutional neural network (DnCNN) is introduced to estimate phase noise and then a modified network called IPDnCNN is constructed for the problem. Based on the IPDnCNN, a novel interferometric phase noise reduction algorithm is proposed, which can reduce phase noise while protecting fringe edges and avoid the use of filter windows. Experimental results using simulated and real data are provided to demonstrate the effectiveness of the proposed method

Long-term monitoring of geodynamic surface deformation using SAR interferometry

Thesis (Ph.D.) University of Alaska Fairbanks, 2014Synthetic Aperture Radar Interferometry (InSAR) is a powerful tool to measure surface deformation and is well suited for surveying active volcanoes using historical and existing satellites. However, the value and applicability of InSAR for geodynamic monitoring problems is limited by the influence of temporal decorrelation and electromagnetic path delay variations in the atmosphere, both of which reduce the sensitivity and accuracy of the technique. The aim of this PhD thesis research is: how to optimize the quantity and quality of deformation signals extracted from InSAR stacks that contain only a low number of images in order to facilitate volcano monitoring and the study of their geophysical signatures. In particular, the focus is on methods of mitigating atmospheric artifacts in interferograms by combining time-series InSAR techniques and external atmospheric delay maps derived by Numerical Weather Prediction (NWP) models. In the first chapter of the thesis, the potential of the NWP Weather Research & Forecasting (WRF) model for InSAR data correction has been studied extensively. Forecasted atmospheric delays derived from operational High Resolution Rapid Refresh for the Alaska region (HRRRAK) products have been compared to radiosonding measurements in the first chapter. The result suggests that the HRRR-AK operational products are a good data source for correcting atmospheric delays in spaceborne geodetic radar observations, if the geophysical signal to be observed is larger than 20 mm. In the second chapter, an advanced method for integrating NWP products into the time series InSAR workflow is developed. The efficiency of the algorithm is tested via simulated data experiments, which demonstrate the method outperforms other more conventional methods. In Chapter 3, a geophysical case study is performed by applying the developed algorithm to the active volcanoes of Unimak Island Alaska (Westdahl, Fisher and Shishaldin) for long term volcano deformation monitoring. The volcano source location at Westdahl is determined to be approx. 7 km below sea level and approx. 3.5 km north of the Westdahl peak. This study demonstrates that Fisher caldera has had continuous subsidence over more than 10 years and there is no evident deformation signal around Shishaldin peak.Chapter 1. Performance of the High Resolution Atmospheric Model HRRR-AK for Correcting Geodetic Observations from Spaceborne Radars -- Chapter 2. Robust atmospheric filtering of InSAR data based on numerical weather prediction models -- Chapter 3. Subtle motion long term monitoring of Unimak Island from 2003 to 2010 by advanced time series SAR interferometry -- Chapter 4. Conclusion and future work

Detecting differential ground displacements of civil structures in fast-subsiding metropolises with interferometric SAR and band-pass filtering

Ground displacements due to changes in soil conditions represent a threat to the stability of civil structures in many urban areas, worldwide. In fast-subsiding areas, regional subsidence (wavelength ~ 1,000’s m) can be dominantly high and, consequently, mask other signals at local scales (wavelength ~ 10–100’s m). Still, engineering and construction applications require a comprehensive knowledge of local-scale signals, which can threaten the stability of buildings and infrastructure. Here we present a new technique based on band-pass filters for uncovering local-scale signals hidden by regional subsidence as detected by interferometric SAR measurements. We apply our technique to a velocity field calculated from 21 high-resolution COSMO-SkyMed scenes acquired over Mexico City and obtain components of long (\u3e 478 m), intermediate (42–478 m) and short (\u3c 42 m) spatial wavelengths. Our results reveal that long-wavelength velocities exceed − 400 mm/year, whereas intermediate- and short-wavelength velocities are in the order of ± 15 mm/year. We show that intermediate-wavelength velocities are useful for retrieving signals such as uplift along elevated viaducts of Metro lines 4 and B, as well as differential displacements in Pantitlán station’s pedestrian overpass system and across sharp geotechnical boundaries in the piedmont of Sierra de Santa Catarina—where surface faulting occurs

Decomposing DInSAR Time-Series into 3-D in Combination with GPS in the Case of Low Strain Rates: An Application to the Hyblean Plateau, Sicily, Italy

Differential Interferometric SAR (DInSAR) time-series techniques can be used to derive surface displacement rates with accuracies of 1 mm/year, by measuring the one-dimensional distance change between a satellite and the surface over time. However, the slanted direction of the measurements complicates interpretation of the signal, especially in regions that are subject to multiple deformation processes. The Simultaneous and Integrated Strain Tensor Estimation from Geodetic and Satellite Deformation Measurements (SISTEM) algorithm enables decomposition into a three-dimensional velocity field through joint inversion with GNSS measurements, but has never been applied to interseismic deformation where strain rates are low. Here, we apply SISTEM for the first time to detect tectonic deformation on the Hyblean Foreland Plateau in South-East Sicily. In order to increase the signal-to-noise ratio of the DInSAR data beforehand, we reduce atmospheric InSAR noise using a weather model and combine it with a multi-directional spatial filtering technique. The resultant three-dimensional velocity field allows identification of anthropogenic, as well as tectonic deformation, with sub-centimeter accuracies in areas of sufficient GPS coverage. Our enhanced method allows for a more detailed view of ongoing deformation processes as compared to the single use of either GNSS or DInSAR only and thus is suited to improve assessments of regional seismic hazard

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

InSAR Monitoring of Italian Coastline Revealing Natural and Anthropogenic Ground Deformation Phenomena and Future Perspectives

In this work, we use X and C-band SAR data provided by the COSMO-SkyMed and ENVISAT missions to detect and measure some ground deformation phenomena along six coastal areas of Italy. In particular, we exploit multi-temporal interferometric synthetic aperture radar (InSAR), i.e., small baseline subsets (SBAS) and interferometric point target analysis (IPTA) methods, to retrieve the deformation rate maps and time series for each investigated area. Multi-temporal InSAR analysis revealed local subsidence and uplifting effects in Ravenna Coastal Areas, Fiumicino, Campi Flegrei, Sibari Plain, Augusta Bay, and Taranto Gulf. Our work is meant as a demonstrator to show how InSAR-based analysis can provide a detailed understanding of the coastal hazards. Such analysis also opens up new monitoring scenarios such as the possibility of designing a near real-time surveillance service based on Sentinel-1 SAR data.Publishedid 31522T. Deformazione crostale attivaJCR Journa

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