From 89b9b3678f3b53819eb3adfeda022c74a900d547 Mon Sep 17 00:00:00 2001 From: Scott Staniewicz Date: Thu, 18 Jan 2024 13:35:06 -0500 Subject: [PATCH 1/5] add bibtex extension for mkdocs, start references closes #192 --- docs/getting-started.md | 4 +- docs/references.bib | 89 +++++++++++++++++++++++++++++++++++ docs/requirements.txt | 2 + mkdocs.yml | 6 ++- src/dolphin/phase_link/mle.py | 10 +--- 5 files changed, 101 insertions(+), 10 deletions(-) create mode 100644 docs/references.bib diff --git a/docs/getting-started.md b/docs/getting-started.md index 040edbf2e..9485a81de 100644 --- a/docs/getting-started.md +++ b/docs/getting-started.md @@ -1,6 +1,5 @@ ## Install - `dolphin` is available on conda-forge: ```bash @@ -126,3 +125,6 @@ mkdocs serve ``` then open http://localhost:8000 in your browser. Creating new files or updating existing files will automatically trigger a rebuild of the documentation while `mkdocs serve` is running. + +For citations, use the notation `[@Ansari2018EfficientPhaseEstimation]` to refer to a Bibtex key in `docs/references.bib` (e.g. [@Ansari2018EfficientPhaseEstimation]). +This can be done in either a markdown file, or in a docstring. diff --git a/docs/references.bib b/docs/references.bib new file mode 100644 index 000000000..81041da10 --- /dev/null +++ b/docs/references.bib @@ -0,0 +1,89 @@ +@article{Ansari2017SequentialEstimatorEfficient, + 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}, + abstract = {Wide-swath synthetic aperture radar (SAR) missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric SAR (InSAR) time series. However, the processing of the emerging Big Data is challenging for state-of-the-art InSAR analysis techniques. This contribution introduces a novel approach, named Sequential Estimator, for efficient estimation of the interferometric phase from long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by the compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. Such interferograms are used to link the ``older'' data batches with the most recent acquisitions and thus to reconstruct the phase time series. This scheme avoids the necessity of reprocessing the entire data stack at the face of each new acquisition. The proposed estimator introduces negligible degradation compared to the Cramer-Rao lower bound under realistic coherence scenarios. The estimator may therefore be adapted for high-precision near-real-time processing of InSAR and accommodate the conversion of InSAR from an offline to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to state-of-the-art techniques via simulations and application to Sentinel-1 data.}, + 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}, + abstract = {Signal decorrelation poses a limitation to multipass SAR interferometry. In pursuit of overcoming this limitation to achieve high-precision deformation estimates, different techniques have been developed, with short baseline subset, SqueeSAR, and CAESAR as the overarching schemes. These different analysis approaches raise the question of their efficiency and limitation in phase and consequently deformation estimation. This contribution first addresses this question and then proposes a new estimator with improved performance, called Eigendecomposition-based Maximum-likelihood-estimator of Interferometric phase (EMI). The proposed estimator combines the advantages of the state-of-the-art techniques. Identical to CAESAR, EMI is solved using eigendecomposition; it is therefore computationally efficient and straightforward in implementation. Similar to SqueeSAR, EMI is a maximum-likelihood-estimator; hence, it retains estimation efficiency. The computational and estimation efficiency of EMI renders it as an optimum choice for phase estimation. A further marriage of EMI with the proposed Sequential Estimator by Ansari et al. provides an efficient processing scheme tailored to the analysis of Big InSAR Data. EMI is formulated and verified in relation to the state-of-the-art approaches via mathematical formulation, simulation analysis, and experiments with time series of Sentinel-1 data over the volcanic island of Vulcano, Italy.}, + 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}, + abstract = {This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms.}, + 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{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}, + abstract = {Phase artifacts in interferometric synthetic aperture radar (InSAR) images frequently degrade the interpretability of the phase and correlation signatures of terrain. Often, these distortions are attributed to spatially variable ionospheric propagation delays at two different SAR acquisition times. We present here L-band InSAR data from Iceland, California, and Hawaii. The California and Hawaii interferograms show no significant ionospheric artifacts, while the Iceland interferogram shows a maximum misregistration of three pixels in the azimuth direction, which leads to severe phase decorrelation artifacts in the InSAR image. We relate the misregistration of complex pixels seen in the interferograms to the gradient of the ionospheric total electron content (TEC) observed by global positioning system (GPS) data and confirm that indeed the phase artifacts in the Iceland interferogram are due to dispersive ionospheric propagation rather than other decorrelation factors such as neutral atmospheric delays. We develop a method to measure the spatial TEC variation at synthetic aperture length scales using dual-frequency GPS carrier phase data. We solve for the GPS data ambiguities using a low-resolution ionosphere reference derived from either available ionospheric observations or the GPS carrier phase data themselves. GPS observations show directly the level of ionospheric variability, and the spatial TEC gradient as observed by GPS predicts the misregistration of complex pixels in interferograms in all three areas. This confirmation of the cause of the image artifacts suggests that they can be routinely corrected from the InSAR data alone, provided that the sensor measures the change in TEC along the radar swath.}, + 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{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}, + abstract = {We describe a python package for nonlinear phase linking of full resolution SAR images using both distributed and persistent scatterers. In the workflow, the first step is to find for each pixel the set of self-similar pixels in order to identify persistent and distributed scatterers. Next the phase linking is performed using the full complex coherence matrix containing the wrapped phase values of each distributed scatterer. Our package uses a hybrid approach consisting of eigenvalue decomposition-based maximum likelihood phase linking and the classic eigenvalue decomposition method. The latter is used for pixels with a non-invertible covariance matrix. A sequential mode achieves computational efficiency. The next step is to unwrap the phase by selecting an opti\- mum unwrapping network of interferograms and invert for the unwrapped phase time-series which is converted to the displacement time-series. We show how the performance of phase linking depends on the temporal cor\- relation behavior using simulations of the coherence matrix. The sequential approaches better retrieve the simulated phases compared to the non-sequential approaches for all temporal coherence models. Phase linking methods retrieve the simulated phase with residuals close to the Cram{\textasciiacute}er{\textendash}Rao lower bound for coherent seasons where the absolute values of coherence matrix are high and provide a tool for obtaining InSAR measurements over areas with seasonal snowfall. We furthermore show that unwrapping errors propagate differently depending on the unwrapping network. For single-reference networks there is no error propagation, but for sequential networks it compromises the accuracy of the final displacement time-series. Delaunay networks provide an optimum solution in terms of accuracy and precision if there are several years of data with frequent temporal decorrelation or strong seasonal decorrelation. We present applications using Sentinel-1 data in different natural and anthropogenic environments.}, + langid = {english}, + keywords = {Distributed scatterer,MiaplPy InSAR,Phase linking,Sequential} +} + +@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}, + abstract = {Phase decorrelation, caused by changes in the surface scattering properties between two radar acquisitions, is a major limiting factor for interferometric synthetic aperture radar (InSAR) surface deformation analysis over vegetated terrain. Persistent Scatterer (PS) techniques have been developed to identify high-quality radar pixels suffering from minimal decorrelation artifacts. However, existing PS selection algorithms are often based on the statistics of InSAR amplitude and phase measurements at each individual radar pixel, and scattering signal models that take into account the phase correlation of nearby PS pixels have not been fully developed. Here, we present a new PS selection algorithm based on the similarity of phase observations between nearby radar pixels. We used this algorithm to analyze 25 C-band Envisat SAR scenes acquired over the San Luis Valley, Colorado, and 93 C-band Sentinel-1 SAR scenes acquired over the Greater Houston area, Texas. At both the test sites, the presence of dense vegetation leads to severe phase decorrelation artifacts even in some interferograms with short temporal baselines. Our algorithm can reduce the number of false positive and false negative PS pixels identified from an existing PS identification algorithm. The improved PS identification accuracy allows us to substantially increase the total number of high-quality interferograms that are suitable for time series analysis. We reconstructed spatially coherent InSAR phase observations through an interpolation between PS pixels, and recovered subtle deformation signals that are otherwise undetectable. In both the cases, the superior performance of our PS processing strategy was demonstrated using a large number of independent ground-truth data.}, + 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} +} diff --git a/docs/requirements.txt b/docs/requirements.txt index 99de283a2..ee490ab59 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,4 +1,6 @@ +# For citations: # mkdocs requirements +git+https://github.com/bobmyhill/mdx_bib mkdocs mkdocs-gen-files mkdocs-jupyter diff --git a/mkdocs.yml b/mkdocs.yml index 74652e3c9..0a02398ae 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -29,11 +29,15 @@ plugins: docstring_style: numpy members_order: alphabetical # source - mkdocs-jupyter: - include: ["*.ipynb"] + include: ["docs/notebooks/*.ipynb"] markdown_extensions: - pymdownx.arithmatex: generic: true + - footnotes + - mdx_bib: + bibtex_file: docs/references.bib + order: 'unsorted' extra_javascript: - javascripts/mathjax.js diff --git a/src/dolphin/phase_link/mle.py b/src/dolphin/phase_link/mle.py index 21ae6a59b..611230358 100644 --- a/src/dolphin/phase_link/mle.py +++ b/src/dolphin/phase_link/mle.py @@ -196,6 +196,7 @@ def mle_stack( Will use cupy if available, (and if the input is a GPU array). Otherwise, uses numpy (for CPU version). + Parameters ---------- C_arrays : ndarray, shape = (rows, cols, nslc, nslc) @@ -203,7 +204,7 @@ def mle_stack( (e.g. from [dolphin.phase_link.covariance.estimate_stack_covariance_cpu][]) use_evd : bool, default = False Use eigenvalue decomposition on the covariance matrix instead of - the EMI algorithm. + the EMI algorithm of [@Ansari2018EfficientPhaseEstimation]. beta : float, optional The regularization parameter for inverting Gamma = |C| The regularization is applied as (1 - beta) * Gamma + beta * I @@ -219,13 +220,6 @@ def mle_stack( ------- ndarray, shape = (nslc, rows, cols) The estimated linked phase, same shape as the input slcs (possibly multilooked) - - References - ---------- - [1] Ansari, H., De Zan, F., & Bamler, R. (2018). Efficient phase - estimation for interferogram stacks. IEEE Transactions on - Geoscience and Remote Sensing, 56(7), 4109-4125. - """ xp = get_array_module(C_arrays) # estimate the wrapped phase based on the EMI paper From fb273b4ff9e6e383812e0e24d7f56a992afaad55 Mon Sep 17 00:00:00 2001 From: Scott Staniewicz Date: Thu, 18 Jan 2024 13:35:50 -0500 Subject: [PATCH 2/5] remove abstracts --- docs/references.bib | 6 ------ 1 file changed, 6 deletions(-) diff --git a/docs/references.bib b/docs/references.bib index 81041da10..c75bfcc45 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -10,7 +10,6 @@ @article{Ansari2017SequentialEstimatorEfficient pages = {5637--5652}, issn = {1558-0644}, doi = {10.1109/TGRS.2017.2711037}, - abstract = {Wide-swath synthetic aperture radar (SAR) missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric SAR (InSAR) time series. However, the processing of the emerging Big Data is challenging for state-of-the-art InSAR analysis techniques. This contribution introduces a novel approach, named Sequential Estimator, for efficient estimation of the interferometric phase from long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by the compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. Such interferograms are used to link the ``older'' data batches with the most recent acquisitions and thus to reconstruct the phase time series. This scheme avoids the necessity of reprocessing the entire data stack at the face of each new acquisition. The proposed estimator introduces negligible degradation compared to the Cramer-Rao lower bound under realistic coherence scenarios. The estimator may therefore be adapted for high-precision near-real-time processing of InSAR and accommodate the conversion of InSAR from an offline to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to state-of-the-art techniques via simulations and application to Sentinel-1 data.}, 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} } @@ -25,7 +24,6 @@ @article{Ansari2018EfficientPhaseEstimation pages = {4109--4125}, issn = {1558-0644}, doi = {10.1109/TGRS.2018.2826045}, - abstract = {Signal decorrelation poses a limitation to multipass SAR interferometry. In pursuit of overcoming this limitation to achieve high-precision deformation estimates, different techniques have been developed, with short baseline subset, SqueeSAR, and CAESAR as the overarching schemes. These different analysis approaches raise the question of their efficiency and limitation in phase and consequently deformation estimation. This contribution first addresses this question and then proposes a new estimator with improved performance, called Eigendecomposition-based Maximum-likelihood-estimator of Interferometric phase (EMI). The proposed estimator combines the advantages of the state-of-the-art techniques. Identical to CAESAR, EMI is solved using eigendecomposition; it is therefore computationally efficient and straightforward in implementation. Similar to SqueeSAR, EMI is a maximum-likelihood-estimator; hence, it retains estimation efficiency. The computational and estimation efficiency of EMI renders it as an optimum choice for phase estimation. A further marriage of EMI with the proposed Sequential Estimator by Ansari et al. provides an efficient processing scheme tailored to the analysis of Big InSAR Data. EMI is formulated and verified in relation to the state-of-the-art approaches via mathematical formulation, simulation analysis, and experiments with time series of Sentinel-1 data over the volcanic island of Vulcano, Italy.}, 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} } @@ -40,7 +38,6 @@ @article{Ansari2021StudySystematicBias pages = {1285--1301}, issn = {1558-0644}, doi = {10.1109/TGRS.2020.3003421}, - abstract = {This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms.}, 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} } @@ -55,7 +52,6 @@ @article{Chen2012IonosphericArtifactsSimultaneous pages = {1227--1239}, issn = {1558-0644}, doi = {10.1109/TGRS.2011.2164805}, - abstract = {Phase artifacts in interferometric synthetic aperture radar (InSAR) images frequently degrade the interpretability of the phase and correlation signatures of terrain. Often, these distortions are attributed to spatially variable ionospheric propagation delays at two different SAR acquisition times. We present here L-band InSAR data from Iceland, California, and Hawaii. The California and Hawaii interferograms show no significant ionospheric artifacts, while the Iceland interferogram shows a maximum misregistration of three pixels in the azimuth direction, which leads to severe phase decorrelation artifacts in the InSAR image. We relate the misregistration of complex pixels seen in the interferograms to the gradient of the ionospheric total electron content (TEC) observed by global positioning system (GPS) data and confirm that indeed the phase artifacts in the Iceland interferogram are due to dispersive ionospheric propagation rather than other decorrelation factors such as neutral atmospheric delays. We develop a method to measure the spatial TEC variation at synthetic aperture length scales using dual-frequency GPS carrier phase data. We solve for the GPS data ambiguities using a low-resolution ionosphere reference derived from either available ionospheric observations or the GPS carrier phase data themselves. GPS observations show directly the level of ionospheric variability, and the spatial TEC gradient as observed by GPS predicts the misregistration of complex pixels in interferograms in all three areas. This confirmation of the cause of the image artifacts suggests that they can be routinely corrected from the InSAR data alone, provided that the sensor measures the change in TEC along the radar swath.}, 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)} } @@ -70,7 +66,6 @@ @article{Mirzaee2023NonlinearPhaseLinking issn = {00983004}, doi = {10.1016/j.cageo.2022.105291}, urldate = {2023-03-08}, - abstract = {We describe a python package for nonlinear phase linking of full resolution SAR images using both distributed and persistent scatterers. In the workflow, the first step is to find for each pixel the set of self-similar pixels in order to identify persistent and distributed scatterers. Next the phase linking is performed using the full complex coherence matrix containing the wrapped phase values of each distributed scatterer. Our package uses a hybrid approach consisting of eigenvalue decomposition-based maximum likelihood phase linking and the classic eigenvalue decomposition method. The latter is used for pixels with a non-invertible covariance matrix. A sequential mode achieves computational efficiency. The next step is to unwrap the phase by selecting an opti\- mum unwrapping network of interferograms and invert for the unwrapped phase time-series which is converted to the displacement time-series. We show how the performance of phase linking depends on the temporal cor\- relation behavior using simulations of the coherence matrix. The sequential approaches better retrieve the simulated phases compared to the non-sequential approaches for all temporal coherence models. Phase linking methods retrieve the simulated phase with residuals close to the Cram{\textasciiacute}er{\textendash}Rao lower bound for coherent seasons where the absolute values of coherence matrix are high and provide a tool for obtaining InSAR measurements over areas with seasonal snowfall. We furthermore show that unwrapping errors propagate differently depending on the unwrapping network. For single-reference networks there is no error propagation, but for sequential networks it compromises the accuracy of the final displacement time-series. Delaunay networks provide an optimum solution in terms of accuracy and precision if there are several years of data with frequent temporal decorrelation or strong seasonal decorrelation. We present applications using Sentinel-1 data in different natural and anthropogenic environments.}, langid = {english}, keywords = {Distributed scatterer,MiaplPy InSAR,Phase linking,Sequential} } @@ -84,6 +79,5 @@ @article{Wang2022AccuratePersistentScatterer pages = {1--13}, issn = {1558-0644}, doi = {10.1109/TGRS.2022.3210868}, - abstract = {Phase decorrelation, caused by changes in the surface scattering properties between two radar acquisitions, is a major limiting factor for interferometric synthetic aperture radar (InSAR) surface deformation analysis over vegetated terrain. Persistent Scatterer (PS) techniques have been developed to identify high-quality radar pixels suffering from minimal decorrelation artifacts. However, existing PS selection algorithms are often based on the statistics of InSAR amplitude and phase measurements at each individual radar pixel, and scattering signal models that take into account the phase correlation of nearby PS pixels have not been fully developed. Here, we present a new PS selection algorithm based on the similarity of phase observations between nearby radar pixels. We used this algorithm to analyze 25 C-band Envisat SAR scenes acquired over the San Luis Valley, Colorado, and 93 C-band Sentinel-1 SAR scenes acquired over the Greater Houston area, Texas. At both the test sites, the presence of dense vegetation leads to severe phase decorrelation artifacts even in some interferograms with short temporal baselines. Our algorithm can reduce the number of false positive and false negative PS pixels identified from an existing PS identification algorithm. The improved PS identification accuracy allows us to substantially increase the total number of high-quality interferograms that are suitable for time series analysis. We reconstructed spatially coherent InSAR phase observations through an interpolation between PS pixels, and recovered subtle deformation signals that are otherwise undetectable. In both the cases, the superior performance of our PS processing strategy was demonstrated using a large number of independent ground-truth data.}, 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} } From ee47b0a1fe08e9f1a431ebd8e2a8d0835ce1d83c Mon Sep 17 00:00:00 2001 From: Scott Staniewicz Date: Thu, 18 Jan 2024 13:54:30 -0500 Subject: [PATCH 3/5] fix git url for PEP508 https://peps.python.org/pep-0508/ --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index ee490ab59..c9205f5a0 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,6 +1,6 @@ # For citations: # mkdocs requirements -git+https://github.com/bobmyhill/mdx_bib +mdx_bib @ git+https://github.com/bobmyhill/mdx_bib.git@6b13bbbc407617a5e93ed0f8a0e5e4c52f73f677 mkdocs mkdocs-gen-files mkdocs-jupyter From dec2a549fa8486b421a0bd29568f0f6d2a999ad9 Mon Sep 17 00:00:00 2001 From: Scott Staniewicz Date: Thu, 18 Jan 2024 14:03:42 -0500 Subject: [PATCH 4/5] fix tropo docstring for "Confusing indentation for continuation" --- src/dolphin/atmosphere/troposphere.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/dolphin/atmosphere/troposphere.py b/src/dolphin/atmosphere/troposphere.py index 69d19f747..01c495561 100644 --- a/src/dolphin/atmosphere/troposphere.py +++ b/src/dolphin/atmosphere/troposphere.py @@ -302,7 +302,7 @@ def compute_pyaps(delay_parameters: DelayParams) -> np.ndarray: Returns ------- np.ndarray - tropospheric delay datacube. + tropospheric delay datacube. """ import pyaps3 as pa @@ -377,7 +377,7 @@ def compute_raider(delay_parameters: DelayParams) -> np.ndarray: Returns ------- np.ndarray - tropospheric delay datacube. + tropospheric delay datacube. """ from RAiDER.delay import tropo_delay as raider_tropo_delay from RAiDER.llreader import BoundingBox From 90acb58e1329f21b61c1dde668c14dbc7a16cf08 Mon Sep 17 00:00:00 2001 From: Scott Staniewicz Date: Thu, 18 Jan 2024 14:21:42 -0500 Subject: [PATCH 5/5] add more references for EMI/SHP --- docs/references.bib | 70 +++++++++++++++++++++++++++++ src/dolphin/phase_link/__init__.py | 5 ++- src/dolphin/shp/__init__.py | 3 ++ src/dolphin/shp/_glrt.py | 9 +--- src/dolphin/workflows/sequential.py | 6 +-- 5 files changed, 79 insertions(+), 14 deletions(-) diff --git a/docs/references.bib b/docs/references.bib index c75bfcc45..c7557bcd5 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -55,6 +55,35 @@ @article{Chen2012IonosphericArtifactsSimultaneous 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{Fornaro2015CAESARApproachBased, + title = {{{CAESAR}}: {{An Approach Based}} on {{Covariance Matrix Decomposition}} to {{Improve Multibaseline}}{\textendash}{{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} +} + +@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}, + 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{Mirzaee2023NonlinearPhaseLinking, title = {Non-Linear Phase Linking Using Joined Distributed and Persistent Scatterers}, author = {Mirzaee, Sara and Amelung, Falk and Fattahi, Heresh}, @@ -70,6 +99,36 @@ @article{Mirzaee2023NonlinearPhaseLinking keywords = {Distributed scatterer,MiaplPy InSAR,Phase linking,Sequential} } +@article{Parizzi2011AdaptiveInSARStack, + 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)} +} + +@article{Siddiqui1962ProblemsConnectedRayleigh, + 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}, + urldate = {2023-05-03}, + langid = {english} +} + @article{Wang2022AccuratePersistentScatterer, title = {Accurate {{Persistent Scatterer Identification Based}} on {{Phase Similarity}} of {{Radar Pixels}}}, author = {Wang, Ke and Chen, Jingyi}, @@ -81,3 +140,14 @@ @article{Wang2022AccuratePersistentScatterer 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{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}, + keywords = {Coherence,Decorrelation,Dispersion,Eigenvalues and eigenfunctions,Estimation,History,Snow} +} diff --git a/src/dolphin/phase_link/__init__.py b/src/dolphin/phase_link/__init__.py index e353b2b29..ea1aa1d3a 100644 --- a/src/dolphin/phase_link/__init__.py +++ b/src/dolphin/phase_link/__init__.py @@ -1,7 +1,8 @@ """Package for phase linking stacks of SLCs. -Currently implements the eigenvalue-based maximum likelihood (EMI) -algorithm from (Ansari, 2018). +Currently implements the eigenvalue-based maximum likelihood (EMI) algorithm from +[@Ansari2018EfficientPhaseEstimation], as well as the EVD based approach from +[@Fornaro2015CAESARApproachBased] and [@Mirzaee2023NonlinearPhaseLinking] """ from ._compress import compress # noqa: F401 diff --git a/src/dolphin/shp/__init__.py b/src/dolphin/shp/__init__.py index e8e146104..1953f27c7 100644 --- a/src/dolphin/shp/__init__.py +++ b/src/dolphin/shp/__init__.py @@ -30,6 +30,9 @@ def estimate_neighbors( ) -> np.ndarray: """Estimate the statistically similar neighbors of each pixel. + GLRT method on the [@Parizzi2011AdaptiveInSARStack]. + Assumes Rayleigh distributed amplitudes ([@Siddiqui1962ProblemsConnectedRayleigh]). + Parameters ---------- halfwin_rowcol : Tuple[int, int] diff --git a/src/dolphin/shp/_glrt.py b/src/dolphin/shp/_glrt.py index 219a1e407..be46ef3eb 100644 --- a/src/dolphin/shp/_glrt.py +++ b/src/dolphin/shp/_glrt.py @@ -32,7 +32,8 @@ def estimate_neighbors( ): """Estimate the number of neighbors based on the GLRT. - Assumes Rayleigh distributed amplitudes, based on the method described [1]_. + Based on the method described in [@Parizzi2011AdaptiveInSARStack]. + Assumes Rayleigh distributed amplitudes ([@Siddiqui1962ProblemsConnectedRayleigh]) Parameters ---------- @@ -73,12 +74,6 @@ def estimate_neighbors( `[dolphin.io.compute_out_shape][]` `window_rows = 2 * halfwin_rowcol[0] + 1` `window_cols = 2 * halfwin_rowcol[1] + 1` - - References - ---------- - [1] Parizzi and Brcic, 2011, "Adaptive InSAR Stack Multilooking Exploiting - Amplitude Statistics" - [2] Siddiqui, M. M. (1962). Some problems connected with Rayleigh distributions. """ half_row, half_col = halfwin_rowcol rows, cols = mean.shape diff --git a/src/dolphin/workflows/sequential.py b/src/dolphin/workflows/sequential.py index 0b5fb78a7..cf88610cc 100644 --- a/src/dolphin/workflows/sequential.py +++ b/src/dolphin/workflows/sequential.py @@ -1,10 +1,6 @@ """Estimate wrapped phase using batches of ministacks. -References ----------- - [1] Ansari, H., De Zan, F., & Bamler, R. (2017). Sequential estimator: Toward - efficient InSAR time series analysis. IEEE Transactions on Geoscience and - Remote Sensing, 55(10), 5637-5652. +Initially based on [@Ansari2017SequentialEstimatorEfficient]. """ from __future__ import annotations