Coherency Matrix Decomposition-Based Polarimetric Persistent Scatterer Interferometry

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The rationale of polarimetric optimization techniques is to enhance the phase quality of the interferograms by combining adequately the different polarization channels available to produce an improved one. Different approaches have been proposed for polarimetric persistent scatterer interferometry (PolPSI). They range from the simple and computationally efficient BEST, where, for each pixel, the polarimetric channel with the best response in terms of phase quality is selected, to those with high-computational burden like the equal scattering mechanism (ESM) and the suboptimum scattering mechanism (SOM). BEST is fast and simple, but it does not fully exploit the potentials of polarimetry. On the other side, ESM explores all the space of solutions and finds the optimal one but with a very high-computational burden. A new PolPSI algorithm, named coherency matrix decomposition-based PolPSI (CMD-PolPSI), is proposed to achieve a compromise between phase optimization and computational cost. Its core idea is utilizing the polarimetric synthetic aperture radar (PolSAR) coherency matrix decomposition to determine the optimal polarization channel for each pixel. Three different PolSAR image sets of both full- (Barcelona) and dual-polarization (Murcia and Mexico City) are used to evaluate the performance of CMD-PolPSI. The results show that CMD-PolPSI presents better optimization results than the BEST method by using either $D_{\mathrm{ A}}$ or temporal mean coherence as phase quality metrics. Compared with the ESM algorithm, CMD-PolPSI is 255 times faster but its performance is not optimal. The influence of the number of available polarization channels and pixel's resolutions on the CMD-PolPSI performance is also discussed.Peer ReviewedPostprint (author's final draft

Amplitude-Driven-Adaptive-Neighbourhood Filtering of High-Resolution Pol-InSAR Information

International audienceIn this paper a new method for fltering coherency matrices issued from Synthetic Aperture Radar (SAR) polarimetric interferometric data is presented. For each pixel of the interferogram, an adaptive neighborhood is determined by a region growing technique driven exclusively by the amplitude image information. All the available amplitude images of the interferometric couple are fused in the region growing process to ensure the stationarity hypothesis of the derived statistical population. In addition, for preserving local stationarity requirement of the interferogram, a phase compensation step is performed. Afterwards, all the pixels within the obtained adaptive neighborhood are complex averaged to yield the fltered values of the polarimetric and interferometric coherency matrices. The method has been tested on airborne high-resolution polarimetric interferometric SAR images (Oberpfaffenhofen area - German Space Agency). For comparison purposes, the standard phase compensated fixed multi-look flter and the linear adaptive coherence flter proposed by Lee at al. were also implemented. Both subjective and objective performance analysis, including coherence edge detection, ROC graph and bias reduction tables, recommends the proposed algorithm as a powerful post-processing POL-InSAR tool

Trace Coherence: A New Operator for Polarimetric and Interferometric SAR images

Quadratic forms play an important role in the development of several Polarimetric and Interferometric Synthetic Aperture Radar (Pol-InSAR) methodologies, which are very powerful tools for Earth Observation. This work investigates integrals of Pol-InSAR operators based on quadratic forms, with special interest for the Pol-InSAR coherence. A new operator is introduced, namely Trace Coherence, that provides an approximation for the center of mass of the Coherence Region (CoRe). The latter is the locus of points on the polar plot containing all the possible coherence values. Such center of mass can be calculated as the integral of Pol-InSAR coherences over the scattering mechanisms. The Trace Coherence provides a synthetic information regarding the partial target as one single entity. Therefore, it provides a representation, which is not dependent on the selection of one specific polarization channel. It may find application in change detection (e.g. Coherent Change Detection and differential DEM), classification (e.g. building structure parameters) and modeling (e.g. for the retrieval of forest height). In calculating the integral of the Pol-InSAR coherences, an approximate Trace Coherence expression is derived and shown to improve the calculation speed by several orders of magnitude. The Trace Coherence approximation is investigated using Monte Carlo simulations and validated ESA (DLR) L-band quad-polarimetric data acquired during the AGRISAR 2006 campaign. The result of the analysis using simulated and real data is that the average error in approximating the integral of the Coherence Region is 0.025 in magnitude and 3° in phase (in scenarios with sufficiently high coherence)

Modeling of Subsurface Scattering from Ice Sheets for Pol-InSAR Applications

Remote sensing is a fundamental tool to measure the dynamics of ice sheets and provides valuable information for ice sheet projections under a changing climate. There is, however, the potential to further reduce the uncertainties in these projections by developing innovative remote sensing methods. One of these remote sensing techniques, the polarimetric synthetic aperture radar interferometry (Pol-InSAR), is known since decades to have the potential to assess the geophysical properties below the surface of ice sheets, because of the penetration of microwave signals into dry snow, firn, and ice. Despite this, only very few studies have addressed this topic and the development of robust Pol-InSAR applications is at an early stage. Two potential Pol-InSAR applications are identified as the motivation for this thesis. First, the estimation and compensation of the penetration bias in digital elevation models derived with SAR interferometry. This bias can lead to errors of several meters or even tens of meters in surface elevation measurements. Second, the estimation of geophysical properties of the subsurface of glaciers and ice sheets using Pol-InSAR techniques. There is indeed potential to derive information about melt-refreeze processes within the firn, which are related to density and affect the mass balance. Such Pol-InSAR applications can be a valuable information source with the potential for monthly ice sheet wide coverage and high spatial resolution provided by the next generation of SAR satellites. However, the required models to link the Pol-InSAR measurements to the subsurface properties are not yet established. The aim of this thesis is to improve the modeling of the vertical backscattering distribution in the subsurface of ice sheets and its effect on polarimetric interferometric SAR measurements at different frequencies. In order to achieve this, polarimetric interferometric multi-baseline SAR data at different frequencies and from two different test sites on the Greenland ice sheet are investigated. This thesis contributes with three concepts to a better understanding and to a more accurate modeling of the vertical backscattering distribution in the subsurface of ice sheets. First, the integration of scattering from distinct subsurface layers. These are formed by refrozen melt water in the upper percolation zone and cause an interesting coherence undulation pattern, which cannot be explained with previously existing models. This represents a first link between Pol-InSAR data and geophysical subsurface properties. The second step is the improved modeling of the general vertical backscattering distribution of the subsurface volume. The advantages of more flexible volume models are demonstrated, but interestingly, the simple modification of a previously existing model with a vertical shift parameter lead to the best agreement between model and data. The third contribution is the model based compensation of the penetration bias, which is experimentally validated. At the investigated test sites, it becomes evident that the model based estimates of the surface elevations are more accurate than the interferometric phase center locations, which are conventionally used to derive surface elevations of ice sheets. This thesis therefore improves the state of the art of subsurface scattering modeling for Pol-InSAR applications, demonstrates the model-based penetration bias compensation, and makes a further research step towards the retrieval of geophysical subsurface information with Pol-InSAR

Investigation of polarimetric coherence optimization in persistent scatterer interferometry

"December 2010.""A Thesis presented to the Faculty of the Graduate School University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Justin J. Legarsky.Interferometric Synthetic Aperture Radar (InSAR) provides a means of imaging small deformations of the Earth's surface (millimeter scale). Furthermore, interferometric point target analysis (IPTA) and time-series analyses can be enhanced using polarimetric and interferometric synthetic aperture radar (PolInSAR) processing techniques to improve detection of point targets or coherent scatterers (CS) that are characterized by a point-like scattering behavior. A mixture of single or multiple scattering mechanisms with a high range of backscattering amplitudes characterize many SAR images over a number of areas. However, the fact that a number of scatterers are characterized by a strongly polarized behavior and are located at different heights even within the same resolution cell make the combination of polarimetric and interferometric information a promising one. For the study site of Socorro, New Mexico, full polarization techniques provided a significant increase (i.e. more than doubled) in CSs compared to a non full polarization. Scattering mechanism and temporal behavior of the CSs were investigated. The CS scattering mechanism behavior was found consistent amongst the CS lists. Full polarization processing was found to increase the CS quantity significantly compared to single polarizations while maintaining high temporal coherence. Thus, this significant increase in full polarization CSs has the potential to increase the overall InSAR processing quality. For sites that challenge (e.g. vegetated areas and other low coherence environments) the single polarization InSAR processing, the full polarization increase in CSs may be essential for processing.Includes bibliographical references (pages 55-56)

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR\u27s airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/170

Monitoring Water Level Change and Seasonal Vegetation Change in the Coastal Wetlands of Louisiana Using L-Band Time-Series

Coastal wetlands are productive ecosystems driven by highly dynamic hydrological processes such as tides and river discharge, which operate at daily to seasonal timescales, respectively. The scientific community has been calling for landscape-scale measurements of hydrological variables that could help understand the flow of water and transport of sediment across coastal wetlands. While in situ water level gauge data have enabled significant advances, they are limited in coverage and largely unavailable in many parts of the world. In preparation for the NISAR mission, we investigate the use of spaceborne Interferometric Synthetic Aperture Radar (InSAR) observations of phase and coherence at L-band for landscape-scale monitoring of water level change and vegetation cover in coastal wetlands across seasons. We use L-band SAR images acquired by ALOS/PALSAR from 2007 to 2011 to study the impact of seasonal changes in vegetation cover on InSAR sensitivity to water level change in the wetlands of the Atchafalaya basin located in coastal Louisiana, USA. Seasonal variations are observed in the interferometric coherence (γ) time-series over wetlands, with higher coherence during the winter and lower coherence during the summer. We show with InSAR time-series that coherence is inversely correlated with Normalized Difference Vegetation Index (NDVI). Our analysis of polarimetric scattering mechanisms demonstrates that double-bounce is the dominant mechanism in swamps while its weakness in marshes hinders estimation of water level changes. In swamps, water level change maps derived from InSAR are highly correlated (r2 = 0.83) with in situ data from the Coastwide Reference Monitoring System (CRMS). From October to December, we observed that the water level may be below wetland elevation and thus not inundating wetlands significantly. Our analysis shows that water level can only be retrieved when both images used for InSAR are acquired when wetlands are inundated. The L-band derived-maps of water level change show large scale gradients originating from the Gulf Intracoastal Waterway rather than the main delta trunk channel, confirming its significant role as a source of hydrologic connectivity across these coastal wetlands. These results indicate that NISAR, with its InSAR observations every 12 days, will provide the measurements necessary to reveal large scale hydrodynamic processes that occur in swamps across seasons

Monitoring Water Level Change and Seasonal Vegetation Change in the Coastal Wetlands of Louisiana Using L-Band Time-Series

Coastal wetlands are productive ecosystems driven by highly dynamic hydrological processes such as tides and river discharge, which operate at daily to seasonal timescales, respectively. The scientific community has been calling for landscape-scale measurements of hydrological variables that could help understand the flow of water and transport of sediment across coastal wetlands. While in situ water level gauge data have enabled significant advances, they are limited in coverage and largely unavailable in many parts of the world. In preparation for the NISAR mission, we investigate the use of spaceborne Interferometric Synthetic Aperture Radar (InSAR) observations of phase and coherence at L-band for landscape-scale monitoring of water level change and vegetation cover in coastal wetlands across seasons. We use L-band SAR images acquired by ALOS/PALSAR from 2007 to 2011 to study the impact of seasonal changes in vegetation cover on InSAR sensitivity to water level change in the wetlands of the Atchafalaya basin located in coastal Louisiana, USA. Seasonal variations are observed in the interferometric coherence (γ) time-series over wetlands, with higher coherence during the winter and lower coherence during the summer. We show with InSAR time-series that coherence is inversely correlated with Normalized Difference Vegetation Index (NDVI). Our analysis of polarimetric scattering mechanisms demonstrates that double-bounce is the dominant mechanism in swamps while its weakness in marshes hinders estimation of water level changes. In swamps, water level change maps derived from InSAR are highly correlated (r2 = 0.83) with in situ data from the Coastwide Reference Monitoring System (CRMS). From October to December, we observed that the water level may be below wetland elevation and thus not inundating wetlands significantly. Our analysis shows that water level can only be retrieved when both images used for InSAR are acquired when wetlands are inundated. The L-band derived-maps of water level change show large scale gradients originating from the Gulf Intracoastal Waterway rather than the main delta trunk channel, confirming its significant role as a source of hydrologic connectivity across these coastal wetlands. These results indicate that NISAR, with its InSAR observations every 12 days, will provide the measurements necessary to reveal large scale hydrodynamic processes that occur in swamps across seasons

Wetland Monitoring and Mapping Using Synthetic Aperture Radar

Wetlands are critical for ensuring healthy aquatic systems, preventing soil erosion, and securing groundwater reservoirs. Also, they provide habitat for many animal and plant species. Thus, the continuous monitoring and mapping of wetlands is necessary for observing effects of climate change and ensuring a healthy environment. Synthetic Aperture Radar (SAR) remote sensing satellites are active remote sensing instruments essential for monitoring wetlands, given the possibility to bypass the cloud-sensitive optical instruments and obtain satellite imagery day and night. Therefore, the purpose of this chapter is to provide an overview of the basic concepts of SAR remote sensing technology and its applications for wetland monitoring and mapping. Emphasis is given to SAR systems with full and compact polarimetric SAR capabilities. Brief discussions on the latest state-of-the-art wetland applications using SAR imagery are presented. Also, we summarize the current trends in wetland monitoring and mapping using SAR imagery. This chapter provides a good introduction to interested readers with limited background in SAR technology and its possible wetland applications