Hu et al., 2022 - Google Patents
Isolating orbital error from multitemporal InSAR derived tectonic deformation based on wavelet and independent component analysisHu et al., 2022
- Document ID
- 9792130414614814379
- Author
- Hu J
- Zhu K
- Fu H
- Liu J
- Wang C
- Gui R
- Publication year
- Publication venue
- IEEE Geoscience and Remote Sensing Letters
External Links
Snippet
Isolating the orbital error from the interferometric synthetic aperture radar (InSAR) observations is a great challenge, especially in the presence of tectonic deformation due to their similar spatial patterns. The influence of orbital error is systematic, which can reduce …
- 238000004458 analytical method 0 title abstract description 22
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- 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
- 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
- G01S13/9023—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. correcting range migration errors combined with monopulse or interferometric techniques
-
- 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
- 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
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shirzaei | A wavelet-based multitemporal DInSAR algorithm for monitoring ground surface motion | |
| Zhang et al. | A novel multitemporal InSAR model for joint estimation of deformation rates and orbital errors | |
| Xu et al. | Toward absolute phase change recovery with InSAR: Correcting for earth tides and phase unwrapping ambiguities | |
| Hu et al. | Isolating orbital error from multitemporal InSAR derived tectonic deformation based on wavelet and independent component analysis | |
| Liu et al. | Joint correction of ionosphere noise and orbital error in L-band SAR interferometry of interseismic deformation in southern California | |
| Yu et al. | Optimal baseline design for multibaseline InSAR phase unwrapping | |
| Samsonov et al. | Multidimensional Small Baseline Subset (MSBAS) for volcano monitoring in two dimensions: Opportunities and challenges. Case study Piton de la Fournaise volcano | |
| CN116363057B (en) | A surface deformation extraction method that combines PCA and time series InSAR | |
| Ma et al. | A sequential approach for Sentinel-1 TOPS time-series co-registration over low coherence scenarios | |
| Xu et al. | Kinematic coregistration of sentinel-1 TOPSAR images based on sequential least squares adjustment | |
| Wang et al. | Near real-time InSAR deformation time series estimation with modified Kalman filter and sequential least squares | |
| Mao et al. | An InSAR ionospheric correction method based on variance component estimation with integration of MAI and RSS measurements | |
| Zhang et al. | Verification of the accuracy of Sentinel-1 for DEM extraction error analysis under complex terrain conditions | |
| Zhao et al. | Psmnet: A neural network-driven approach for pixel similarity measurement in distributed scatterer interferometry | |
| Cao et al. | High‐resolution water vapor maps obtained by merging interferometric synthetic aperture radar and GPS measurements | |
| Manunta et al. | A novel algorithm based on compressive sensing to mitigate phase unwrapping errors in multitemporal DInSAR approaches | |
| CN112363165A (en) | Method, device, equipment and medium for forest subsurface shape inversion | |
| Li et al. | A deep-learning neural network for postseismic deformation reconstruction from InSAR time series | |
| Charrier et al. | Analysis of dense coregistration methods applied to optical and SAR time-series for ice flow estimations | |
| Feng | Modelling co-and post-seismic displacements revealed by InSAR, and their implications for fault behaviour | |
| Guliaev et al. | On the Use of Tomographically Derived Reflectivity Profiles for Pol-InSAR Forest Height Inversion in the Context of the BIOMASS Mission | |
| CN114966681A (en) | A Soil Moisture Estimation Method Based on Atmospheric Correction C-band InSAR Data | |
| Ma et al. | A new spatiotemporal InSAR tropospheric noise filtering: An interseismic case study over central san Andreas fault | |
| Yang et al. | Heterogeneous InSAR tropospheric correction based on local texture correlation | |
| Liu et al. | Dynamically estimating deformations with wrapped InSAR based on sequential adjustment |