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paper/paper.md

@@ -51,7 +51,7 @@ InSAR has been a powerful tool for decades, both in geophysical studies includin
 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
 
 <!-- A list of key references, including to other software addressing related needs. -->
-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.
 
@@ -60,7 +60,7 @@ While these tools represent significant progress, there remained a need for soft
 `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:
 
 - 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 `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.