Khaki et al., 2019 - Google Patents
A sequential Monte Carlo framework for noise filtering in InSAR time seriesKhaki et al., 2019
View PDF- Document ID
- 9539505197310324913
- Author
- Khaki M
- Filmer M
- Featherstone W
- Kuhn M
- Bui L
- Parker A
- Publication year
- Publication venue
- IEEE Transactions on Geoscience and Remote Sensing
External Links
Snippet
This article proposes an alternative filtering technique to improve interferometric synthetic aperture radar (InSAR) time series by reducing residual noise while retaining the ground deformation signal. To this end, for the first time, a data-driven approach is introduced, which …
- 238000001914 filtration 0 title abstract description 37
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. correcting range migration errors
- G01S13/9035—Particular SAR processing techniques not provided for elsewhere, e.g. squint mode, doppler beam-sharpening mode, spotlight mode, bistatic SAR, inverse SAR
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V99/00—Subject matter not provided for in other groups of this subclass
- G01V99/005—Geomodels or geomodelling, not related to particular measurements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Khaki et al. | A sequential Monte Carlo framework for noise filtering in InSAR time series | |
| Bagnardi et al. | Inversion of surface deformation data for rapid estimates of source parameters and uncertainties: A Bayesian approach | |
| Oubanas et al. | Discharge estimation in ungauged basins through variational data assimilation: The potential of the SWOT mission | |
| Oubanas et al. | River discharge estimation from synthetic SWOT-type observations using variational data assimilation and the full Saint-Venant hydraulic model | |
| Lion et al. | Determination of a high spatial resolution geopotential model using atomic clock comparisons | |
| Dalaison et al. | A Kalman filter time series analysis method for InSAR | |
| Schumann et al. | Exploiting the proliferation of current and future satellite observations of rivers. | |
| Ubelmann et al. | Dynamic mapping of along-track ocean altimetry: Method and performance from observing system simulation experiments | |
| Wang et al. | Interferometric synthetic aperture radar statistical inference in deformation measurement and geophysical inversion: A review | |
| Grombein et al. | On high-frequency topography-implied gravity signals for a height system unification using GOCE-based global geopotential models | |
| Flores et al. | Hydrologic data assimilation with a hillslope‐scale‐resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter | |
| Camacho et al. | 3D multi-source model of elastic volcanic ground deformation | |
| Conroy et al. | Probabilistic estimation of InSAR displacement phase guided by contextual information and artificial intelligence | |
| Tasan et al. | Leveraging GNSS tropospheric products for machine learning-based land subsidence prediction | |
| Crosetto et al. | Land deformation measurement using SAR interferometry: state-of-the-art | |
| Rodríguez-Molina et al. | Time-scales of inter-eruptive volcano uplift signals: Three Sisters volcanic center, Oregon (United States) | |
| Vavra et al. | Active dipping interface of the Southern San Andreas fault revealed by space geodetic and seismic imaging | |
| Maurya et al. | Site scale landslide deformation and strain analysis using MT-InSAR and GNSS approach–A case study | |
| Zhang et al. | Time series land subsidence monitoring and prediction based on SBAS-InSAR and GeoTemporal transformer model | |
| Rosenburg et al. | Topographic power spectra of cratered terrains: Theory and application to the Moon | |
| Bagheri et al. | Assessment of land subsidence using interferometric synthetic aperture radar time series analysis and artificial neural network in a geospatial information system: case study of Rafsanjan Plain | |
| Manunta et al. | A novel algorithm based on compressive sensing to mitigate phase unwrapping errors in multitemporal DInSAR approaches | |
| Biswas et al. | Spatial-correlation based persistent scatterer interferometric study for ground deformation | |
| Crosetto et al. | State of the art of land deformation monitoring using differential SAR interferometry | |
| Khaki et al. | Assimilation of grace follow‐on inter‐satellite laser ranging measurements into land surface models |