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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

Abstract Transit spectroscopy is a powerful tool for decoding the chemical compositions of the atmospheres of extrasolar planets. In this paper, we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. After cleaning and validating the data, we demonstrate methods for: (i) initial exploratory data analysis, based on summary statistics (estimates of location and variability); (ii) exploring and quantifying the existing correlations in the data; (iii) preprocessing and linearly transforming the data to its principal components; (iv) dimensionality reduction and manifold learning; (v) clustering and anomaly detection; and (vi) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in a fully unsupervised fashion. We advocate for lower-dimensional representations of the spectroscopic data in terms of the main principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.

Matchev, Konstantin T. (ORCID:0000000341829096)↗
NASA NTRS1985

Neogene compressional deformation and possible thrust faulting in southwest Dominican Republic

Analysis of regional and high resolution remote sensing data coupled with detailed field investigations indicates Neogene compressional deformation in the southwest Dominican Republic. Airborne synthetic aperture radar data and high resolution near infrared photography show folds in Tertiary sediments and possible thrust fault scarps implying NE to SW compression in the region. Large road cuts through the scarps allow study of otherwise poorly accessible, heavily vegetated karst terrain. Deformation increases toward scrap fronts where small bedding-plane thrust faults become more numerous. Analysis of mesoscopic faults with slickensides indicates compression oriented between N to S and E to W. The lowermost scarp has highly sheared fault breccia and undeformed frontal talus breccias implying it is the basal thrust into which the higher thrust faults sole. Thus, the scarps probably formed in a regional NE to SW compressional stress regime and are the toes of thrust sheets. Previous workers have suggested that these scarps are ancient shorelines. However, the gross morphology of the scarps differs substantially from well known erosional terraces on the north coast.

Golombek, M. P.↗
NASA NTRS1994

Measurement of aerodynamic roughness using radar backscatter over vegetated surfaces

The importance of the measurement of wind fields is discussed. Wind regime data can be used to infer the amount and type of wind induced (aerolian) transport of sand and dust, or to establish global circulation models, for example on other planets. Since local measurements are costly and often impossible, it is desired to infer such data from remotely sensed information. A potential mechanism for remotely inferring the wind regime by using synthetic aperture radar data to describe the roughness of the surface is described. A project to estimate the practicality of using such a mechanism is described. An experiment that extends the mechanism to vegetated sites, where the goal is to measure potential for erosion, is reported.

Wall, S. D.↗
NASA NTRS1989

Resolution experiments for NW Pacific subduction zone tomography

Results are reported from an investigation of the resolving power of ISC/NEIC P travel-time data in tomographic inversions for the geometry of the subduction zones in the NW Pacific. From thermal models for the Kurile, Janan, Izu-Bonin, Mariana, and Ryukyu slabs, three-dimensional synthetic velocity anomalies for subducting slabs are generated and projected onto a cell model for the uppermost 1400 km of the mantle. These synthetic models are used to compute synthetic delay times for ray paths corresponding to the source and receiver locations used for the actual data, add Gaussian noise, invert the synthetic data, and compare the resulting velocity structure to the initial synthetic models. This comparison is illustrated for sections through the Kuriles and the Mariana arcs. A variety of resolution artifacts are observed, which in many cases resemble features visible in the tomographic results obtained from inverting the actual ISC/NEIC data.

Spakman, Wim↗
NASA NTRS2011

Techniques and Tools for Estimating Ionospheric Effects in Interferometric and Polarimetric SAR Data

The InSAR Scientific Computing Environment (ISCE) is a flexible, extensible software tool designed for the end-to-end processing and analysis of synthetic aperture radar data. ISCE inherits the core of the ROI_PAC interferometric tool, but contains improvements at all levels of the radar processing chain, including a modular and extensible architecture, new focusing approach, better geocoding of the data, handling of multi-polarization data, radiometric calibration, and estimation and correction of ionospheric effects. In this paper we describe the characteristics of ISCE with emphasis on the ionospheric modules. To detect ionospheric anomalies, ISCE implements the Faraday rotation method using quadpolarimetric images, and the split-spectrum technique using interferometric single-, dual- and quad-polarimetric images. The ability to generate co-registered time series of quad-polarimetric images makes ISCE also an ideal tool to be used for polarimetric-interferometric radar applications.

Ionosphere↗
NASA NTRS1985

CV-990 L-band SAR: A calibration experiment

Calibrated image data is required by most users of synthetic aperture radar (SAR) data particularly those attempting to classify targets based upon their radar backscatter signature as a function of frequency polarization or incidence angle. In this experiment, the backscatter derived by calibrating the NASA/JPL CV-990 L-band SAR, and the backscatter reported from a pass of the NASA/JSC C-130 scatterometer as the two instruments flew over the same site at different times are compared.

Held, D. N.↗
NASA NTRS1995

Accurate estimation of sigma(exp 0) using AIRSAR data

During recent years signature analysis, classification, and modeling of Synthetic Aperture Radar (SAR) data as well as estimation of geophysical parameters from SAR data have received a great deal of interest. An important requirement for the quantitative use of SAR data is the accurate estimation of the backscattering coefficient sigma(exp 0). In terrain with relief variations radar signals are distorted due to the projection of the scene topography into the slant range-Doppler plane. The effect of these variations is to change the physical size of the scattering area, leading to errors in the radar backscatter values and incidence angle. For this reason the local incidence angle, derived from sensor position and Digital Elevation Model (DEM) data must always be considered. Especially in the airborne case, the antenna gain pattern can be an additional source of radiometric error, because the radar look angle is not known precisely as a result of the the aircraft motions and the local surface topography. Consequently, radiometric distortions due to the antenna gain pattern must also be corrected for each resolution cell, by taking into account aircraft displacements (position and attitude) and position of the backscatter element, defined by the DEM data. In this paper, a method to derive an accurate estimation of the backscattering coefficient using NASA/JPL AIRSAR data is presented. The results are evaluated in terms of geometric accuracy, radiometric variations of sigma(exp 0), and precision of the estimated forest biomass.

Holecz, Francesco↗
NASA NTRS2023

Commercial Smallsat Data Acquisition Program On-ramp #2 Airbus U.S. Synthetic Aperture Radar (SAR) Evaluation Report

In 2017, NASA’s Earth Science Division (ESD) launched the Private-Sector Small Constellation Satellite Data Product Pilot, now referred to as the Commercial Smallsat Data Acquisition (CSDA) program. The objective of CSDA is to identify, evaluate, and acquire commercial remote sensing data that support NASA’s Earth science research and application activities. The Pilot successfully concluded in early 2020, when CSDA transitioned into a sustained program with on-ramping opportunities for new vendors as the industry emerges with new candidates and capabilities. In October 2019, a Request for Information (RFI) seeking capability statements from parties interested in providing data from spaceborne platforms was released for the CSDA on-ramp #2 evaluations. To be responsive to the RFI, the commercial satellite constellations had to consist of three or more operating spacecraft actively collecting data in a non-geostationary orbit with full latitudinal coverage and be U.S. companies. Two vendors responded to the RFI and were evaluated by a committee composed of NASA ESD leadership, program managers, and scientists. Both vendors satisfied the RFI requirements and were asked to respond to a Request for Proposal (RFP). After review of the proposals, NASA entered into a Blanket Purchase Agreement (BPA) with Airbus Defense and Space GEO, Inc. (Airbus) U.S. in September 2021 and with BlackSky Geospatial Solutions, Inc. (BlackSky) in November 2021. In this report, CSDA provides an evaluation of the usefulness of data provided by the Airbus U.S. Synthetic Aperture Radar (SAR) satellite constellation, consisting of TerraSAR-X (launched in 2007), TanDEM-X (launched in 2010), and PAZ (launched in 2018), for advancing NASA’s Earth system science research and applications. The evaluation of the BlackSky commercial data will be provided in a separate report. To conduct the Airbus evaluation, NASA’s ESD augmented 13 existing research projects that could potentially benefit from, and had the expertise to evaluate, the commercial data being considered for longer-term purchase. Investigators from NASA’s Research and Analysis Program science focus areas and from NASA’s Applied Sciences Program elements participated in the evaluation. A summary of the research areas evaluated by the Principal Investigator (PI) teams is presented in Figure 3. CSDA also funded a dedicated activity to evaluate the satellite data quality (calibration and geolocation) independently by assessing the accuracy of data from Airbus. Evaluation activities were carried out by the selected PIs from December 7, 2022, to December 7, 2023. Delivery of datasets requested by the researchers began in January 2023. The vendors were evaluated on the accessibility of data, accuracy and completeness of metadata, and promptness and quality of user support services. Datasets purchased during the evaluation have been archived by NASA and will be made available to current and future government-funded researchers in accordance with the End User License Agreement (EULA). This synthesis report distills and integrates the findings of research reports commissioned by NASA for the Airbus evaluation. This report also includes recommendations that inform the way ahead for the program. The scientific results from the evaluations demonstrated that the commercial data from Airbus were able to advance NASA research and applications. However, the PIs encountered limitations that diminished the usefulness of the data due to the amount of effort that was required to access, preprocess, and analyze these data. One significant issue encountered was the limited spatial and temporal coverage of the data in the Airbus archive that could be used to conduct time series analyses or assessments over large spatial scales. Overall, however, the utility and the quality of the evaluated data outweighed the difficulties encountered, and NASA has concluded that the Airbus SAR data would complement NASA’s existing Earth observation capabilities and Airbus U.S. would qualify to participate in the sustained phase of the program.

Batuhan Osmanoglu↗
DOE OSTI2021

Matrix Completion for Improved Observability in Low-Voltage Distribution Grids

This paper considers the problem of recovering missing entries in a partially observed matrix from relatively few measurements (i.e., the so-called matrix completion problem) with the aim of increasing the presently limited observability of low-voltage distribution grids. To this end, the partially observed matrix is formed using scarce voltage magnitude measurements while accounting for their spatial information. Voltage readings are assumed to be collected from distribution utility sensors and/or geographically-distributed cable television network sensors located in immediate proximity to distribution grid nodes. A matrix completion approach built on the parameter-less singular value shrinkage technique is used to estimate voltage magnitudes at otherwise non-observable low-voltage nodes using a small number of single- or multiple-snapshot data. The effectiveness of the proposed approach is demonstrated using a U.S.-style distribution test system from the synthetic SMART- DS data set under very low- to moderate-observability conditions.

low-rank matrix completion↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
NASA NTRS1982

Multisensor data analysis and its application to monitoring of cropland, forest, strip mines and cultural targets

Seasat L-band and aircraft X-band dual polarized synthetic aperture (SAR) data of the Western Kentucky Coal Region were examined, preprocessed and combined with Landsat Multispectral Scanner (MSS) data to form a seven-band multisensor data set. Analysis of classified data sets show that the three-band SAR data contain moderate discrimination accuracy for the strip mine class but low accuracy for the residential class. The four-band MSS data contain low classification accuracy for the strip mine and residential classes. The integrated five-band SAR/MSS data show that significant improvement in classification accuracy is obtained for both strip mine and residential classes.

Wu, S. T.↗
DOE OSTI2021

Application of the DNAS framework expansion to occupant population synthesis

Research in occupant behaviour is now using a more elaborate framework of building occupant interaction. Researchers often face challenges in collecting data, particularly for the data to meet the minimum number of required data points and the data interoperability requirements. Researchers address the first issue with the synthetic population and the latter with data ontologies. While synthetic population is commonly used to address the first issue, data ontology development is used to address the latter. The two solutions are complementary to each other. One of the known ontologies in building occupant behaviour research is the Drivers-Needs-Actions-Systems (DNAS) ontology, which has been used by building modelers to describe energy-related occupant behaviour. This paper describes the ontology-based synthetic population generation that can be used in the agent-based modeling (ABM) applications. This paper considers multiple data sources, including ASHRAE Thermal Comfort DB II and IEA Annex 66 data sets. A case study of an office building is used to present the workflow of DNAS framework expansion, synthetic population generation, and agent-based modeling.

Chandra Putra, Handi↗
DOE OSTI2022

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Experiments with the Mesoscale Atmospheric Simulation System (MASS) using the synthetic relative humidity

This study is intended to examine the impact of the synthetic relative humidity on the model simulation of mesoscale convective storm environment. The synthetic relative humidity is derived from the National Weather Services surface observations, and non-conventional sources including aircraft, radar, and satellite observations. The latter sources provide the mesoscale data of very high spatial and temporal resolution. The synthetic humidity data is used to complement the National Weather Services rawinsonde observations. It is believed that a realistic representation of initial moisture field in a mesoscale model is critical for the model simulation of thunderstorm development, and the formation of non-convective clouds as well as their effects on the surface energy budget. The impact will be investigated based on a real-data case study using the mesoscale atmospheric simulation system developed by Mesoscale Environmental Simulations Operations, Inc. The mesoscale atmospheric simulation system consists of objective analysis and initialization codes, and the coarse-mesh and fine-mesh dynamic prediction models. Both models are a three dimensional, primitive equation model containing the essential moist physics for simulating and forecasting mesoscale convective processes in the atmosphere. The modeling system is currently implemented at the Applied Meteorology Unit, Kennedy Space Center. Two procedures involving the synthetic relative humidity to define the model initial moisture fields are considered. It is proposed to perform several short-range (approximately 6 hours) comparative coarse-mesh simulation experiments with and without the synthetic data. They are aimed at revealing the model sensitivities should allow us both to refine the specification of the observational requirements, and to develop more accurate and efficient objective analysis schemes. The goal is to advance the MASS (Mesoscal Atmospheric Simulation System) modeling expertise so that the model output can provide reliable guidance for thunderstorm forecasting.

Chang, Chia-Bo↗
NASA NTRS1991

Integration of visible-through microwave-range multispectral image data sets for geologic mapping

Multispectral remote sensing data sets collected during the Geologic Remote Sensing Field Experiment (GRSFE) conducted during 1989 in the southwestern U.S. were used to produce thematic image maps showing details of the surface geology. LANDSAT TM (Thematic Mapper) images were used to map the distribution of clays, carbonates, and iron oxides. AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data were used to identify and map calcite, dolomite, sericite, hematite, and geothite, including mixtures. TIMS (Thermal Infrared Multispectral Scanner) data were used to map the distribution of igneous rock phases and carbonates based on their silica contents. AIRSAR (Airborne Synthetic Aperture Radar) data were used to map surface textures related to the scale of surface roughness. The AIRSAR also allowed identification of previously unmapped fault segments and structural control of lithology and minerology. Because all of the above data sets were geographically referenced, combination of different data types and direct comparison of the results with conventional field and laboratory data sets allowed improved geologic mapping of the test site.

Kruse, Fred A.↗
NASA NTRS1991

The AIRSAR image browse system

The volume of data acquired by synthetic aperture radar (SAR) missions motivates the development of a technique that will provide investigators with timely access to image data, and the capability to visually inspect images to determine various characteristics of the data collected, as well as the extent of the data coverage. A system was designed for accessing and browsing an on-line archive of NASA Airborne SAR (AIRSAR) imagery and is being developed as an extension to the SAR Data Catalog System (SDCS). The AIRSAR browse system consists of three elements: (1) an on-line, compressed imagery archive; (2) a user interface for selecting and accessing the on-line images; and (3) a software application program for reconstructuring and displaying the images. Details of these elements are described. The goals are to maximize the utilization of the AIRSAR data set, and to demonstrate the image browse concept for SAR data so that baseline catalog systems supporting SIR-C and future SAR missions will feature this capability.

Pang, A.↗
DOE OSTI2023

Inversion-based correction of Double-Torsion (DT) subcritical crack growth tests for crack profile geometry

Because of its simplicity and the ability to produce a stable, slow-propagating crack, the Double-Torsion (DT) method has been used widely for investigating the critical and subcritical propagation of a slow-propagating tensile (mode-I) crack. However, to determine the complex relationship between the crack velocity $\mathcal{v_c}$ vs. the strain energy release rate $\mathscr{G}$ (or the stress intensity factor K) from laboratory measurements, several corrections must be made to account for the impact of sample and crack geometry. Particularly, DT test typically produces a crack with a curved edge profile instead of a straight line, causing the local $\mathcal{v_c}$ and $\mathscr{G}$ vary along the crack front. The experimentally measured $\mathcal{v_c}$ and $\mathscr{G}$ data merely reflect collective, averaged behavior of the crack. This makes inversion for the intrinsic, “true” crack growth kinetics necessary, based upon the knowledge of the crack geometry. Simple and effective correction methods have been proposed and validated for the slow, chemical-reaction-controlled part (Region I) of the $\mathcal{v_c}$-$\mathscr{G}$ curve. However, reliable methods for the highly nonlinear, transport-dominated part (Region II) and its sudden transition to the dynamic propagation part (Region III) are still lacking. Here we propose a method for determining the intrinsic $\mathcal{v_c}$-$\mathscr{G}$ relationship across all three Regions based upon DT test data, using a simple model function and its numerical inversion. The performance of this approach is examined and demonstrated using both synthetic and laboratory data for subcritical crack growth in soda lime glass.

42 ENGINEERING↗
NASA NTRS2008

Observations and Mitigation of RFI in ALOS PALSAR SAR Data; Implications for the Desdyni Mission

Initial examination of ALOS PALSAR synthetic aperture radar (SAR) data has indicated significant radio frequency interference (RFI) in several geographic locations around the world. RFI causes significant reduction in image contrast, introduces periodic and quasi-periodic image artifacts, and introduces significant phase noise in repeat pass interferometric data reduction. The US National Research Council Decadal Survey of Earth Science has recommended DESDynI, a Deformation, Ecosystems, and Dynamics of Ice satellite mission comprising an L-band polarimetric radar configured for repeat pass interferometry. There is considerable interest internationally in other future L-band and lower frequency systems as well. Therefore the issues of prevalence and possibilities of mitigation of RFI in these crowded frequency bands is of considerable interest. RFI is observed in ALOS PALSAR in California, USA, and in southern Egypt in data examined to date. Application of several techniques for removing it from the data prior to SAR image formation, ranging from straightforward spectral normalization to time-domain, multi-phase filtering techniques are considered. Considerable experience has been gained from the removal of RFI from P-band acquired by the GeoSAR system. These techniques applied to the PALSAR data are most successful when the bandwidth of any particular spectral component of the RFI is narrow. Performance impacts for SAR imagery and interferograms are considered in the context of DESDynI measurement requirements.

InSAR↗