diff --git a/docs/references.bib b/docs/references.bib index fdb1e6a4..6bef6a41 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -100,14 +100,29 @@ @article{Chen2015PersistentScattererInterpolation } @article{Fattahi2019FRInGEFullResolutionInSAR, - title = {{{FRInGE}}; {{Full-Resolution InSAR}} Timeseries Using {{Generalized Eigenvectors}}}, - author = {Fattahi, H. and Agram, P. S. and Tymofyeyeva, E. and Bekaert, D. P.}, - year = {2019}, - month = dec, + author = {{Fattahi}, H. and {Agram}, P.S. and {Tymofyeyeva}, E. and {Bekaert}, D.P.}, + title = {{FRInGE; Full-Resolution InSAR timeseries using Generalized Eigenvectors}}, + keywords = {1209 Tectonic deformation, GEODESY AND GRAVITY, 1211 Non-tectonic deformation, GEODESY AND GRAVITY, 1240 Satellite geodesy: results, GEODESY AND GRAVITY, 1241 Satellite geodesy: technical issues, GEODESY AND GRAVITY}, + booktitle = {AGU Fall Meeting Abstracts}, + year = 2019, volume = {2019}, + month = dec, + eid = {G11B-0514}, pages = {G11B-0514}, - urldate = {2024-05-16}, - keywords = {1209 Tectonic deformation,1211 Non-tectonic deformation,1240 Satellite geodesy: results,1241 Satellite geodesy: technical issues,GEODESY AND GRAVITY} + adsurl = {https://ui.adsabs.harvard.edu/abs/2019AGUFM.G11B0514F}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@article{Ferretti2001PermanentScattersSAR, + title = {Permanent {{Scatters}} in {{SAR Interferometry}}}, + author = {Ferretti, Alessandro and Prati, Claudio and Rocca, Fabrio}, + year = {2001}, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {39}, + number = {1}, + pages = {8--20}, + issn = {01962892}, + doi = {10.1109/36.898661} } @article{Ferretti2011NewAlgorithmProcessing, @@ -140,6 +155,18 @@ @article{Fornaro2015CAESARApproachBased keywords = {3-D,4-D and multidimensional (Multi-D) SAR imaging,Covariance matrices,Covariance matrix decomposition,differential SAR tomography,differential synthetic aperture radar (SAR) interferometry (DInSAR),Interferometry,Monitoring,principal component analysis (PCA),SAR interferometry (InSAR),SAR tomography,Scattering,Spatial resolution,Synthetic aperture radar,Tomography} } +@article{Goldstein1998RadarInterferogramFiltering, + title = {Radar Interferogram Filtering for Geophysical Applications}, + author = {Goldstein, Richard M. and Werner, Charles L.}, + year = {1998}, + journal = {Geophysical Research Letters}, + volume = {25}, + number = {21}, + pages = {4035--4038}, + issn = {1944-8007}, + doi = {10.1029/1998GL900033} +} + @article{Guarnieri2008ExploitationTargetStatistics, title = {On the {{Exploitation}} of {{Target Statistics}} for {{SAR Interferometry Applications}}}, author = {Guarnieri, A. M. and Tebaldini, S.}, @@ -211,7 +238,7 @@ @inproceedings{Rosen2018InSARScientificComputing publisher = {IEEE}, address = {Valencia}, doi = {10.1109/IGARSS.2018.8517504}, - langid = {english}, + langid = {english} } @article{Siddiqui1962ProblemsConnectedRayleigh, diff --git a/paper/paper.md b/paper/paper.md index a412871a..2b15ba1b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -39,37 +39,39 @@ bibliography: references.bib # Summary - Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique used for measuring land surface deformation. Conventional InSAR uses pairs of SAR images to get a single map of the relative displacement between the two acquisition times. -`dolphin` is a Python library which uses state-of-the-art multi-temporal algorithms to reduce the impact of noise sources and produce long time series of displacement at fine resolution. +Dolphin is a Python library which uses state-of-the-art multi-temporal algorithms to reduce the impact of noise sources and produce long time series of displacement at fine resolution. ![Average surface displacement velocity along the radar line-of-sight between February, 2017 and December, 2020. Red (blue) indicates motion towards (away from) the satellite.\label{fig:mojave}](figures/bristol-velocity-sequential.png) # Statement of need - -InSAR has been a powerful tool for decades, both in geophysical studies including tectonics, volcanism, and glacier dynamics, as well as human applications such as urban development, mining, and groundwater extraction. The launch of the European Space Agency's Sentinel-1 satellite in 2014 dramatically increased the availability of free, open-access SAR data. However, processing InSAR data has remained challenging, particularly for non-experts. +InSAR has been a powerful tool for decades, both in geophysical studies including tectonics, volcanism, and glacier dynamics, as well as human applications such as urban development, mining, and groundwater extraction. The launch of the European Space Agency's Sentinel-1 satellite in 2014 dramatically increased the availability of free, open-access SAR data. However, processing InSAR data remains challenging, particularly for non-experts. Advanced algorithms combining persistent scatterer (PS) and distributed scatterer (DS) techniques, also known as phase linking, have been developed over the past decade to help overcome decorrelation noise in longer time series [@Guarnieri2008ExploitationTargetStatistics]. Despite their potential, these methods have only recently begun to appear in open-source tools. - -The phase linking first prototype was the [`FRInGE`](https://github.com/isce-framework/fringe) C++ library [@Fattahi2019FRInGEFullResolutionInSAR], which implements algorithms and workflows from @Ferretti2011NewAlgorithmProcessing and @Ansari2018EfficientPhaseEstimation. The [`Miaplpy`](https://github.com/insarlab/MiaplPy) Python library contains a superset of the features in `FRInGE`, as well as new algorithms developed in @Mirzaee2023NonlinearPhaseLinking. Additionally, the MATLAB [`TomoSAR`](https://github.com/DinhHoTongMinh/TomoSAR) library was made public in 2022, which implements the "Compressed SAR" (ComSAR) algorithm, a variant of phase linking detailed in @HoTongMinh2022CompressedSARInterferometry. +The phase linking first prototype was the [FRInGE](https://github.com/isce-framework/fringe) C++ library [@Fattahi2019FRInGEFullResolutionInSAR], which implements algorithms and workflows from @Ferretti2011NewAlgorithmProcessing and @Ansari2018EfficientPhaseEstimation. The [MiaplPy](https://github.com/insarlab/MiaplPy) Python library contains a superset of the features in FRInGE, as well as new algorithms developed in @Mirzaee2023NonlinearPhaseLinking. Additionally, the MATLAB [TomoSAR](https://github.com/DinhHoTongMinh/TomoSAR) library was made public in 2022, which implements the "Compressed SAR" (ComSAR) algorithm, a variant of phase linking detailed in @HoTongMinh2022CompressedSARInterferometry. -While these tools represent significant progress, there remained a need for software capable of handling the heavy computational demands of large-scale InSAR processing. `dolphin` was developed to meet this need, specifically for the Observational Products for End-Users from Remote Sensing Analysis (OPERA) project. OPERA, a Jet Propulsion Laboratory project funded by the Satellite Needs Working Group (SNWG), is tasked with generating a North American Surface Displacement product covering over 10 million square kilometers of land at 30 meter resolution or finer, with under 72 hours of latency. +While these tools represent significant progress, there remained a need for software capable of handling the heavy computational demands of large-scale InSAR processing. For example, the TomoSAR library currently requires tens of gigabytes of memory to process more than a small area of interest, while FRInGE and MiaplPy are unable to offer speedups to users who want to process data at a coarser output grid than the full SLC resolution. Additionally, both FRInGE and MiaplPy were designed to process single batches of SLC images. + +Dolphin was developed to process both historical archives and incrementally handle new data in near-real time. This capability was specifically designed for the Observational Products for End-Users from Remote Sensing Analysis (OPERA) project. OPERA, a Jet Propulsion Laboratory project funded by the Satellite Needs Working Group (SNWG), is tasked with generating a North American Surface Displacement product covering over 10 million square kilometers of land at 30 meter resolution or finer, with under 72 hours of latency. # Overview of Dolphin -`dolphin` processes stacks of coregistered single-look complex (SLC) radar images into a time series of surface displacement. The software has pre-made workflows accessible through command line tools which call core algorithms for PS/DS processing: +Dolphin processes coregistered single-look complex (SLC) radar images into a time series of surface displacement. The software has an end-to-end surface displacement processing workflow (\autoref{fig:overview}), accessible through a command line tool, which calls core algorithms for PS/DS processing: - The `shp` subpackage estimates the SAR backscatter distribution to find neighborhoods of statistically homogeneous pixels (SHPs) using the generalized likelihood ratio test from @Parizzi2011AdaptiveInSARStack or the Kolmogorov-Smirnov test from @Ferretti2011NewAlgorithmProcessing. - The `phase_link` subpackage processes the complex SAR covariance matrix into a time series of wrapped phase using the CAESAR algorithm [@Fornaro2015CAESARApproachBased], the eigenvalue-based maximum likelihood estimator of interferometric phase (EMI) [@Ansari2018EfficientPhaseEstimation], or the combined phase linking (CPL) approach from @Mirzaee2023NonlinearPhaseLinking. -- The `unwrap` subpackage exposes multiple phase unwrapping algorithms, including the Statistical-cost, Network-flow Algorithm for Phase Unwrapping (SNAPHU) [@Chen2001TwodimensionalPhaseUnwrapping] and the PHASS algorithm (available in the InSAR Scientific Computing Environment [@Rosen2018InSARScientificComputing]). -- The `timeseries` module contains basic functionality to invert an overdetermined network of unwrapped interferograms into a time series and estimate the average surface velocity. The outputs of `dolphin` are also compatible with the Miami INsar Time-series software for users who are already comfortable with MintPy [@Yunjun2019SmallBaselineInSAR]. +- The `ps` module selects persistent scatterer pixels from the full-resolution SLCs to be integrated into the wrapped interferograms [@Ferretti2001PermanentScattersSAR]. +- The `unwrap` subpackage exposes multiple phase unwrapping algorithms, including the Statistical-cost, Network-flow Algorithm for Phase Unwrapping (SNAPHU) [@Chen2001TwodimensionalPhaseUnwrapping], the PHASS algorithm (available in the InSAR Scientific Computing Environment [@Rosen2018InSARScientificComputing]), and the Extended Minimum Cost Flow (EMCF) 3D phase unwrapping algorithm via the `spurt` library. Dolphin has pre- and post-processing options, including Goldstein filtering [@Goldstein1998RadarInterferogramFiltering] or interferogram masking and interpolation [@Chen2015PersistentScattererInterpolation]. +- The `timeseries` module contains basic functionality to invert an overdetermined network of unwrapped interferograms into a time series and estimate the average surface velocity. The outputs of Dolphin are also compatible with the Miami INsar Time-series software for users who are already comfortable with MintPy [@Yunjun2019SmallBaselineInSAR]. + +To meet the computational demands of large-scale InSAR processing, Dolphin leverages Just-in-time (JIT) compilation, maintaining the readability of Python while matching the speed of compiled languages. The software's compute-intensive routines use the XLA compiler within JAX [@Bradbury2018JAXComposableTransformations] for efficient CPU or GPU processing. Users with compatible GPUs can see 5-20x speedups by simply installing additional packages. Dolphin manages memory efficiently through batch processing and multi-threaded I/O, allowing it to handle datasets larger than available memory while typically using a few gigabytes for most processing stages. These optimizations enable Dolphin to process hundreds of full-frame Sentinel-1 images with minimal configuration, making it well-suited for large-scale projects such as OPERA. -To meet the computational demands of large-scale InSAR processing, `dolphin` leverages Just-in-time (JIT) compilation, maintaining the readability of Python while matching the speed of compiled languages. The software's compute-intensive routines use the XLA compiler within JAX [@Bradbury2018JAXComposableTransformations] for efficient CPU or GPU processing. Users with compatible GPUs can see 5-20x speedups by simply installing additional packages. `dolphin` manages memory efficiently through batch processing and multi-threaded I/O, allowing it to handle datasets larger than available memory while typically using a few gigabytes for most processing stages. These optimizations enable dolphin to process hundreds of full-frame Sentinel-1 images with minimal configuration, making it well-suited for large-scale projects such as OPERA. +![Overview of main workflow to generate surface displacement. Rectangular stacks indicate input or intermediate raster images. Arrows show the flow of data through the configurable submodules of Dolphin.\label{fig:overview}](figures/dolphin-modules.pdf) -The `dolphin` command line tool provides an interface for running the end-to-end displacement workflow. To illustrate, if a user has created a stack of coregistered SLCs in a `data/` directory, they only need to follow two steps to run the full workflow with all default parameters: +The Dolphin command line tool provides an interface for running the end-to-end displacement workflow. To illustrate, if a user has created a stack of coregistered SLCs in a `data/` directory, they only need to follow two steps to run the full workflow with all default parameters: 1. Configure the workflow with the `config` command, indicating the location of the SLCs, which dumps the output to a YAML file: @@ -83,7 +85,9 @@ dolphin config --slc-files data/* dolphin run dolphin_config.yaml ``` -\autoref{fig:mojave} shows an example result of the final average surface velocity map created by `dolphin`. The inputs were OPERA Coregistered Single-Look Complex (CSLC) geocoded images from Sentinel-1 data between February 2017 - December 2020 over the Mojave Desert. +The full set of configuration options can be viewed with the `dolphin config --print-empty` command. + +\autoref{fig:mojave} shows an example result of the final average surface velocity map created by Dolphin. The inputs were OPERA Coregistered Single-Look Complex (CSLC) geocoded images from Sentinel-1 data between February 2017 - December 2020 over the Mojave Desert. # Acknowledgements diff --git a/paper/references.bib b/paper/references.bib index a3aab6da..0cc753ff 100644 --- a/paper/references.bib +++ b/paper/references.bib @@ -1,274 +1,299 @@ @article{Ansari2017SequentialEstimatorEfficient, - title = {Sequential {{Estimator}}: {{Toward Efficient InSAR Time Series Analysis}}}, + title = {Sequential {{Estimator}}: {{Toward Efficient InSAR Time Series Analysis}}}, shorttitle = {Sequential {{Estimator}}}, - author = {Ansari, Homa and De Zan, Francesco and Bamler, Richard}, - year = {2017}, - month = oct, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - volume = {55}, - number = {10}, - pages = {5637--5652}, - issn = {1558-0644}, - doi = {10.1109/TGRS.2017.2711037}, - keywords = {Big Data,Coherence,coherence estimation error,data compression,differential interferometric synthetic aperture radar (DInSAR),distributed scatterers,Earth,efficiency,error analysis,low-rank approximation,Maximum likelihood estimation,maximum-likelihood estimation (MLE),Monitoring,Synthetic aperture radar,Time series analysis} + author = {Ansari, Homa and De Zan, Francesco and Bamler, Richard}, + year = {2017}, + month = oct, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {55}, + number = {10}, + pages = {5637--5652}, + issn = {1558-0644}, + doi = {10.1109/TGRS.2017.2711037}, + keywords = {Big Data,Coherence,coherence estimation error,data compression,differential interferometric synthetic aperture radar (DInSAR),distributed scatterers,Earth,efficiency,error analysis,low-rank approximation,Maximum likelihood estimation,maximum-likelihood estimation (MLE),Monitoring,Synthetic aperture radar,Time series analysis} } @article{Ansari2018EfficientPhaseEstimation, - title = {Efficient {{Phase Estimation}} for {{Interferogram Stacks}}}, - author = {Ansari, Homa and De Zan, Francesco and Bamler, Richard}, - year = {2018}, - month = jul, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - volume = {56}, - number = {7}, - pages = {4109--4125}, - issn = {1558-0644}, - doi = {10.1109/TGRS.2018.2826045}, + title = {Efficient {{Phase Estimation}} for {{Interferogram Stacks}}}, + author = {Ansari, Homa and De Zan, Francesco and Bamler, Richard}, + year = {2018}, + month = jul, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {56}, + number = {7}, + pages = {4109--4125}, + issn = {1558-0644}, + doi = {10.1109/TGRS.2018.2826045}, keywords = {Big Data,coherence matrix,covariance estimation,differential interferometric synthetic aperture radar,distributed scatterers (DS),efficiency,Electromagnetic interference,error analysis,Maximum likelihood estimation,maximum-likelihood estimation,near real-time (NRT) processing,Strain,Synthetic aperture radar,Systematics,Time series analysis} } @article{Ansari2021StudySystematicBias, - title = {Study of {{Systematic Bias}} in {{Measuring Surface Deformation With SAR Interferometry}}}, - author = {Ansari, H. and Zan, F. De and Parizzi, A.}, - year = {2021}, - month = feb, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - volume = {59}, - number = {2}, - pages = {1285--1301}, - issn = {1558-0644}, - doi = {10.1109/TGRS.2020.3003421}, + title = {Study of {{Systematic Bias}} in {{Measuring Surface Deformation With SAR Interferometry}}}, + author = {Ansari, H. and Zan, F. De and Parizzi, A.}, + year = {2021}, + month = feb, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {59}, + number = {2}, + pages = {1285--1301}, + issn = {1558-0644}, + doi = {10.1109/TGRS.2020.3003421}, keywords = {Big Data,Decorrelation,deformation estimation,differential interferometric synthetic aperture radar (SAR) (DInSAR),distributed scatterers (DSs),error analysis,Fading channels,Moisture,near real-time (NRT) processing,phase inconsistencies,signal decorrelation,Strain,Synthetic aperture radar,Systematics,Time series analysis,time-series analysis} } @article{Bekaert2021IntroducingOPERAProject, - title = {Introducing the {{OPERA Project}} for {{Systematic Surface Water}}, {{Surface Deformation}}, and {{Surface Disturbance Data Products}} from {{Satellite Observations}}}, - author = {Bekaert, David and Fattahi, Heresh and Jones, John and Hansen, Matthew and Kwoun, Oh-Ig and Lewis, Steven and Meyer, Franz and Osmanoglu, Batuhan and Marshak, Charles and Hamlington, Benjamin and Bato, Mary Grace and Brancato, Virginia and Shiroma, Gustavo and Jung, Jungkyo and Jai, Benhan and Buckley, Sean and Cruz, Jennifer}, - year = {2021}, - month = dec, - volume = {2021}, - pages = {G42A-07}, + title = {Introducing the {{OPERA Project}} for {{Systematic Surface Water}}, {{Surface Deformation}}, and {{Surface Disturbance Data Products}} from {{Satellite Observations}}}, + author = {Bekaert, David and Fattahi, Heresh and Jones, John and Hansen, Matthew and Kwoun, Oh-Ig and Lewis, Steven and Meyer, Franz and Osmanoglu, Batuhan and Marshak, Charles and Hamlington, Benjamin and Bato, Mary Grace and Brancato, Virginia and Shiroma, Gustavo and Jung, Jungkyo and Jai, Benhan and Buckley, Sean and Cruz, Jennifer}, + year = {2021}, + month = dec, + volume = {2021}, + pages = {G42A-07}, urldate = {2024-05-15} } @misc{Bradbury2018JAXComposableTransformations, - title = {{{JAX}}: Composable Transformations of {{Python}}+{{NumPy}} Programs}, + title = {{{JAX}}: Composable Transformations of {{Python}}+{{NumPy}} Programs}, author = {Bradbury, James and Frostig, Roy and Hawkins, Peter and Johnson, Matthew James and Leary, Chris and Maclaurin, Dougal and Necula, George and Paszke, Adam and VanderPlas, Jake and {Wanderman-Milne}, Skye and Zhang, Qiao}, - year = {2018} + year = {2018} } @article{Chen2001TwodimensionalPhaseUnwrapping, - title = {Two-Dimensional Phase Unwrapping with Use of Statistical Models for Cost Functions in Nonlinear Optimization}, - author = {Chen, Curtis W. and Zebker, Howard A.}, - year = {2001}, - month = feb, + title = {Two-Dimensional Phase Unwrapping with Use of Statistical Models for Cost Functions in Nonlinear Optimization}, + author = {Chen, Curtis W. and Zebker, Howard A.}, + year = {2001}, + month = feb, journal = {Journal of the Optical Society of America A}, - volume = {18}, - number = {2}, - pages = {338}, - issn = {1084-7529, 1520-8532}, - doi = {10.1364/JOSAA.18.000338}, + volume = {18}, + number = {2}, + pages = {338}, + issn = {1084-7529, 1520-8532}, + doi = {10.1364/JOSAA.18.000338}, urldate = {2024-05-16}, - langid = {english} + langid = {english} } @article{Chen2012IonosphericArtifactsSimultaneous, - title = {Ionospheric {{Artifacts}} in {{Simultaneous L-Band InSAR}} and {{GPS Observations}}}, - author = {Chen, Jingyi and Zebker, Howard A.}, - year = {2012}, - month = apr, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - volume = {50}, - number = {4}, - pages = {1227--1239}, - issn = {1558-0644}, - doi = {10.1109/TGRS.2011.2164805}, + title = {Ionospheric {{Artifacts}} in {{Simultaneous L-Band InSAR}} and {{GPS Observations}}}, + author = {Chen, Jingyi and Zebker, Howard A.}, + year = {2012}, + month = apr, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {50}, + number = {4}, + pages = {1227--1239}, + issn = {1558-0644}, + doi = {10.1109/TGRS.2011.2164805}, keywords = {Advanced Land Observation Satellite (ALOS) PhasedArray L-band Synthetic Aperture Radar (PALSAR),Azimuth,California,correlation signatures,Delay,dual-frequency GPS carrier phase data,global positioning system,Global Positioning System,global positioning system (GPS),GPS observations,Hawaii,Iceland,InSAR images,interferograms,interferometric synthetic aperture radar,interpretability,Ionosphere,ionospheric artifacts,ionospheric delay,ionospheric electromagnetic wave propagation,ionospheric propagation delays,ionospheric total electron content,ionospheric variability,L-band InSAR data,L-band InSAR observations,L-band SAR interferometry,neutral atmospheric delays,phase artifacts,pixel misregistration,radar imaging,radar interferometry,radiowave propagation,SAR acquisition times,Satellites,Spaceborne radar,synthetic aperture length scales,synthetic aperture radar,terrain,total electron content (TEC)} } @article{Chen2015PersistentScattererInterpolation, - title = {A Persistent Scatterer Interpolation for Retrieving Accurate Ground Deformation over {{InSAR-decorrelated}} Agricultural Fields}, - author = {Chen, Jingyi and Zebker, Howard A. and Knight, Rosemary}, - year = {2015}, - journal = {Geophysical Research Letters}, - volume = {42}, - number = {21}, - pages = {9294--9301}, - issn = {19448007}, - doi = {10.1002/2015GL065031}, + title = {A Persistent Scatterer Interpolation for Retrieving Accurate Ground Deformation over {{InSAR-decorrelated}} Agricultural Fields}, + author = {Chen, Jingyi and Zebker, Howard A. and Knight, Rosemary}, + year = {2015}, + journal = {Geophysical Research Letters}, + volume = {42}, + number = {21}, + pages = {9294--9301}, + issn = {19448007}, + doi = {10.1002/2015GL065031}, keywords = {decorrelation,groundwater,InSAR deformation map,persistent scatterers} } -@inproceedings{2019AGUFM.G11B0514F, - author = {{Fattahi}, H. and {Agram}, P.S. and {Tymofyeyeva}, E. and {Bekaert}, D.P.}, - title = {{FRInGE; Full-Resolution InSAR timeseries using Generalized Eigenvectors}}, - keywords = {1209 Tectonic deformation, GEODESY AND GRAVITY, 1211 Non-tectonic deformation, GEODESY AND GRAVITY, 1240 Satellite geodesy: results, GEODESY AND GRAVITY, 1241 Satellite geodesy: technical issues, GEODESY AND GRAVITY}, +@article{Fattahi2019FRInGEFullResolutionInSAR, + author = {{Fattahi}, H. and {Agram}, P.S. and {Tymofyeyeva}, E. and {Bekaert}, D.P.}, + title = {{FRInGE; Full-Resolution InSAR timeseries using Generalized Eigenvectors}}, + keywords = {1209 Tectonic deformation, GEODESY AND GRAVITY, 1211 Non-tectonic deformation, GEODESY AND GRAVITY, 1240 Satellite geodesy: results, GEODESY AND GRAVITY, 1241 Satellite geodesy: technical issues, GEODESY AND GRAVITY}, booktitle = {AGU Fall Meeting Abstracts}, - year = 2019, - volume = {2019}, - month = dec, - eid = {G11B-0514}, - pages = {G11B-0514}, - adsurl = {https://ui.adsabs.harvard.edu/abs/2019AGUFM.G11B0514F}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} + year = 2019, + volume = {2019}, + month = dec, + eid = {G11B-0514}, + pages = {G11B-0514}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2019AGUFM.G11B0514F}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} } +@article{Ferretti2001PermanentScattersSAR, + title = {Permanent {{Scatters}} in {{SAR Interferometry}}}, + author = {Ferretti, Alessandro and Prati, Claudio and Rocca, Fabrio}, + year = {2001}, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {39}, + number = {1}, + pages = {8--20}, + issn = {01962892}, + doi = {10.1109/36.898661} +} + @article{Ferretti2011NewAlgorithmProcessing, - title = {A New Algorithm for Processing Interferometric Data-Stacks: {{SqueeSAR}}}, - author = {Ferretti, Alessandro and Fumagalli, Alfio and Novali, Fabrizio and Prati, Claudio and Rocca, Fabio and Rucci, Alessio}, - year = {2011}, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - volume = {49}, - number = {9}, - pages = {3460--3470}, + title = {A New Algorithm for Processing Interferometric Data-Stacks: {{SqueeSAR}}}, + author = {Ferretti, Alessandro and Fumagalli, Alfio and Novali, Fabrizio and Prati, Claudio and Rocca, Fabio and Rucci, Alessio}, + year = {2011}, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {49}, + number = {9}, + pages = {3460--3470}, publisher = {IEEE}, - issn = {01962892}, - doi = {10.1109/TGRS.2011.2124465}, - isbn = {0196-2892}, - keywords = {1pass,Coherence matrix,distributed scatterers (DS),InSAR,permanent scatterers,space-adaptive filtering} + issn = {01962892}, + doi = {10.1109/TGRS.2011.2124465}, + isbn = {0196-2892}, + keywords = {1pass,Coherence matrix,distributed scatterers (DS),InSAR,permanent scatterers,space-adaptive filtering} } @article{Fornaro2015CAESARApproachBased, - title = {{{CAESAR}}: {{An Approach Based}} on {{Covariance Matrix Decomposition}} to {{Improve Multibaseline}}--{{Multitemporal Interferometric SAR Processing}}}, + title = {{{CAESAR}}: {{An Approach Based}} on {{Covariance Matrix Decomposition}} to {{Improve Multibaseline}}--{{Multitemporal Interferometric SAR Processing}}}, shorttitle = {{{CAESAR}}}, - author = {Fornaro, Gianfranco and Verde, Simona and Reale, Diego and Pauciullo, Antonio}, - year = {2015}, - month = apr, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - volume = {53}, - number = {4}, - pages = {2050--2065}, - issn = {1558-0644}, - doi = {10.1109/TGRS.2014.2352853}, - keywords = {3-D,4-D and multidimensional (Multi-D) SAR imaging,Covariance matrices,Covariance matrix decomposition,differential SAR tomography,differential synthetic aperture radar (SAR) interferometry (DInSAR),Interferometry,Monitoring,principal component analysis (PCA),SAR interferometry (InSAR),SAR tomography,Scattering,Spatial resolution,Synthetic aperture radar,Tomography} + author = {Fornaro, Gianfranco and Verde, Simona and Reale, Diego and Pauciullo, Antonio}, + year = {2015}, + month = apr, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {53}, + number = {4}, + pages = {2050--2065}, + issn = {1558-0644}, + doi = {10.1109/TGRS.2014.2352853}, + keywords = {3-D,4-D and multidimensional (Multi-D) SAR imaging,Covariance matrices,Covariance matrix decomposition,differential SAR tomography,differential synthetic aperture radar (SAR) interferometry (DInSAR),Interferometry,Monitoring,principal component analysis (PCA),SAR interferometry (InSAR),SAR tomography,Scattering,Spatial resolution,Synthetic aperture radar,Tomography} +} + + +@article{Goldstein1998RadarInterferogramFiltering, + title = {Radar Interferogram Filtering for Geophysical Applications}, + author = {Goldstein, Richard M. and Werner, Charles L.}, + year = {1998}, + journal = {Geophysical Research Letters}, + volume = {25}, + number = {21}, + pages = {4035--4038}, + issn = {1944-8007}, + doi = {10.1029/1998GL900033} } @article{Guarnieri2008ExploitationTargetStatistics, - title = {On the {{Exploitation}} of {{Target Statistics}} for {{SAR Interferometry Applications}}}, - author = {Guarnieri, A. M. and Tebaldini, S.}, - year = {2008}, - month = nov, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - volume = {46}, - number = {11}, - pages = {3436--3443}, - issn = {0196-2892}, - doi = {10.1109/TGRS.2008.2001756}, + title = {On the {{Exploitation}} of {{Target Statistics}} for {{SAR Interferometry Applications}}}, + author = {Guarnieri, A. M. and Tebaldini, S.}, + year = {2008}, + month = nov, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {46}, + number = {11}, + pages = {3436--3443}, + issn = {0196-2892}, + doi = {10.1109/TGRS.2008.2001756}, keywords = {Decorrelation,decorrelation models,ENVISAT images,geophysical techniques,geophysics computing,image processing,Information retrieval,interferometric phases,Interferometry,line-of-sight displacement,line-of-sight motion,Maximum likelihood estimation,Monte Carlo simulations,multiimage synthetic aperture radar interferometry,Phase estimation,physical parameters,radar interferometry,Radar scattering,remote sensing by radar,residual topography,SAR interferometry applications,Statistical distributions,statistics,Statistics,stochastic processes,Surfaces,synthetic aperture radar,Synthetic aperture radar interferometry,target statistics,topography (Earth),Yield estimation} } @article{HoTongMinh2022CompressedSARInterferometry, - title = {Compressed {{SAR Interferometry}} in the {{Big Data Era}}}, - author = {Ho Tong Minh, Dinh and Ngo, Yen-Nhi}, - year = {2022}, - month = jan, - journal = {Remote Sensing}, - volume = {14}, - number = {2}, - pages = {390}, + title = {Compressed {{SAR Interferometry}} in the {{Big Data Era}}}, + author = {Ho Tong Minh, Dinh and Ngo, Yen-Nhi}, + year = {2022}, + month = jan, + journal = {Remote Sensing}, + volume = {14}, + number = {2}, + pages = {390}, publisher = {Multidisciplinary Digital Publishing Institute}, - issn = {2072-4292}, - doi = {10.3390/rs14020390}, - urldate = {2024-05-15}, + issn = {2072-4292}, + doi = {10.3390/rs14020390}, + urldate = {2024-05-15}, copyright = {http://creativecommons.org/licenses/by/3.0/}, - langid = {english}, - keywords = {ComSAR,InSAR,PSDS,PSI,subsidence,TomoSAR,Vauvert} + langid = {english}, + keywords = {ComSAR,InSAR,PSDS,PSI,subsidence,TomoSAR,Vauvert} } @article{Mirzaee2023NonlinearPhaseLinking, - title = {Non-Linear Phase Linking Using Joined Distributed and Persistent Scatterers}, - author = {Mirzaee, Sara and Amelung, Falk and Fattahi, Heresh}, - year = {2023}, - month = feb, - journal = {Computers \& Geosciences}, - volume = {171}, - pages = {105291}, - issn = {00983004}, - doi = {10.1016/j.cageo.2022.105291}, - urldate = {2023-03-08}, - langid = {english}, + title = {Non-Linear Phase Linking Using Joined Distributed and Persistent Scatterers}, + author = {Mirzaee, Sara and Amelung, Falk and Fattahi, Heresh}, + year = {2023}, + month = feb, + journal = {Computers \& Geosciences}, + volume = {171}, + pages = {105291}, + issn = {00983004}, + doi = {10.1016/j.cageo.2022.105291}, + urldate = {2023-03-08}, + langid = {english}, keywords = {Distributed scatterer,MiaplPy InSAR,Phase linking,Sequential} } @inproceedings{Rosen2018InSARScientificComputing, - author = {Rosen, Paul A. and Gurrola, Eric M. and Agram, Piyush and Cohen, Joshua and Lavalle, Marco and Riel, Bryan V. and Fattahi, Heresh and Aivazis, Michael A.G. and Simons, Mark and Buckley, Sean M.}, + author = {Rosen, Paul A. and Gurrola, Eric M. and Agram, Piyush and Cohen, Joshua and Lavalle, Marco and Riel, Bryan V. and Fattahi, Heresh and Aivazis, Michael A.G. and Simons, Mark and Buckley, Sean M.}, booktitle = {IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium}, - title = {The {{InSAR Scientific Computing Environment}} 3.0: {{A Flexible Framework}} for {{NISAR Operational}} and {{User-Led Science Processing}}}, - year = {2018}, - volume = {}, - number = {}, - pages = {4897-4900}, - keywords = {Python;Radar imaging;Production;Graphics processing units;Orbits;NASA;InSAR processing;geodetic imaging;computational frameworks;Earth science informatics}, - doi = {10.1109/IGARSS.2018.8517504} + title = {The {{InSAR Scientific Computing Environment}} 3.0: {{A Flexible Framework}} for {{NISAR Operational}} and {{User-Led Science Processing}}}, + year = {2018}, + volume = {}, + number = {}, + pages = {4897-4900}, + keywords = {Python;Radar imaging;Production;Graphics processing units;Orbits;NASA;InSAR processing;geodetic imaging;computational frameworks;Earth science informatics}, + doi = {10.1109/IGARSS.2018.8517504} } @article{Parizzi2011AdaptiveInSARStack, - title = {Adaptive {{InSAR Stack Multilooking Exploiting Amplitude Statistics}}: {{A Comparison Between Different Techniques}} and {{Practical Results}}}, + title = {Adaptive {{InSAR Stack Multilooking Exploiting Amplitude Statistics}}: {{A Comparison Between Different Techniques}} and {{Practical Results}}}, shorttitle = {Adaptive {{InSAR Stack Multilooking Exploiting Amplitude Statistics}}}, - author = {Parizzi, Alessandro and Brcic, Ramon}, - year = {2011}, - month = may, - journal = {IEEE Geoscience and Remote Sensing Letters}, - volume = {8}, - number = {3}, - pages = {441--445}, - issn = {1558-0571}, - doi = {10.1109/LGRS.2010.2083631}, - keywords = {adaptive InSAR stack multilooking,Adaptive multilooking,amplitude-based algorithm,backscatter,backscatter amplitude statistics,Coherence,coherence estimation,complex correlation,interferometric phase,interferometric synthetic aperture radar capability,interferometry,Kernel,phase signatures,Pixel,radar backscatter statistics,radar imaging,radar interferometry,Remote sensing,Shape,synthetic aperture radar,Synthetic aperture radar,synthetic aperture radar (SAR)} + author = {Parizzi, Alessandro and Brcic, Ramon}, + year = {2011}, + month = may, + journal = {IEEE Geoscience and Remote Sensing Letters}, + volume = {8}, + number = {3}, + pages = {441--445}, + issn = {1558-0571}, + doi = {10.1109/LGRS.2010.2083631}, + keywords = {adaptive InSAR stack multilooking,Adaptive multilooking,amplitude-based algorithm,backscatter,backscatter amplitude statistics,Coherence,coherence estimation,complex correlation,interferometric phase,interferometric synthetic aperture radar capability,interferometry,Kernel,phase signatures,Pixel,radar backscatter statistics,radar imaging,radar interferometry,Remote sensing,Shape,synthetic aperture radar,Synthetic aperture radar,synthetic aperture radar (SAR)} } @article{Siddiqui1962ProblemsConnectedRayleigh, - title = {Some Problems Connected with {{Rayleigh}} Distributions}, - author = {Siddiqui, M.M.}, - year = {1962}, - month = mar, + title = {Some Problems Connected with {{Rayleigh}} Distributions}, + author = {Siddiqui, M.M.}, + year = {1962}, + month = mar, journal = {Journal of Research of the National Bureau of Standards, Section D: Radio Propagation}, - volume = {66D}, - number = {2}, - pages = {167}, - issn = {1060-1783}, - doi = {10.6028/jres.066D.020}, + volume = {66D}, + number = {2}, + pages = {167}, + issn = {1060-1783}, + doi = {10.6028/jres.066D.020}, urldate = {2023-05-03}, - langid = {english} + langid = {english} } @article{Wang2022AccuratePersistentScatterer, - title = {Accurate {{Persistent Scatterer Identification Based}} on {{Phase Similarity}} of {{Radar Pixels}}}, - author = {Wang, Ke and Chen, Jingyi}, - year = {2022}, - journal = {IEEE Transactions on Geoscience and Remote Sensing}, - volume = {60}, - pages = {1--13}, - issn = {1558-0644}, - doi = {10.1109/TGRS.2022.3210868}, + title = {Accurate {{Persistent Scatterer Identification Based}} on {{Phase Similarity}} of {{Radar Pixels}}}, + author = {Wang, Ke and Chen, Jingyi}, + year = {2022}, + journal = {IEEE Transactions on Geoscience and Remote Sensing}, + volume = {60}, + pages = {1--13}, + issn = {1558-0644}, + doi = {10.1109/TGRS.2022.3210868}, keywords = {Decorrelation,Interferometric Synthetic Aperture Radar (InSAR),Persistent Scatterer (PS),Phase measurement,phase similarity,Radar,Radar measurements,Radar scattering,Strain,surface deformation,Synthetic aperture radar} } @article{Yunjun2019SmallBaselineInSAR, - title = {Small Baseline {{InSAR}} Time Series Analysis: {{Unwrapping}} Error Correction and Noise Reduction}, + title = {Small Baseline {{InSAR}} Time Series Analysis: {{Unwrapping}} Error Correction and Noise Reduction}, shorttitle = {Small Baseline {{InSAR}} Time Series Analysis}, - author = {Yunjun, Zhang and Fattahi, Heresh and Amelung, Falk}, - year = {2019}, - month = dec, - journal = {Computers \& Geosciences}, - volume = {133}, - pages = {104331}, - issn = {00983004}, - doi = {10.1016/j.cageo.2019.104331}, - urldate = {2020-08-14}, - langid = {english} + author = {Yunjun, Zhang and Fattahi, Heresh and Amelung, Falk}, + year = {2019}, + month = dec, + journal = {Computers \& Geosciences}, + volume = {133}, + pages = {104331}, + issn = {00983004}, + doi = {10.1016/j.cageo.2019.104331}, + urldate = {2020-08-14}, + langid = {english} } @article{Zwieback2022CheapValidRegularizers, - title = {Cheap, Valid Regularizers for Improved Interferometric Phase Linking}, - author = {Zwieback, S.}, - year = {2022}, - journal = {IEEE Geoscience and Remote Sensing Letters}, - pages = {1--1}, - issn = {1558-0571}, - doi = {10.1109/LGRS.2022.3197423}, + title = {Cheap, Valid Regularizers for Improved Interferometric Phase Linking}, + author = {Zwieback, S.}, + year = {2022}, + journal = {IEEE Geoscience and Remote Sensing Letters}, + pages = {1--1}, + issn = {1558-0571}, + doi = {10.1109/LGRS.2022.3197423}, keywords = {Coherence,Decorrelation,Dispersion,Eigenvalues and eigenfunctions,Estimation,History,Snow} }