Publications about 'synthetic data'

Articles in journal or book chapters

1. Kaiyu Zhang, Xiaolei Lv, Huiming Chai, and Jingchuan Yao. Unsupervised SAR Image Change Detection for Few Changed Area Based on Histogram Fitting Error Minimization. IEEE Transactions on Geoscience and Remote Sensing, 60:1-19, 2022. Keyword(s): Synthetic aperture radar, Remote sensing, Radar polarimetry, Change detection algorithms, Histograms, Rail transportation, Radiometry, Change detection, conditional random fields (CRFs), half-normal distribution, image thresholding, synthetic aperture radar (SAR) images, unsupervised change detection. [Abstract] [bibtex-entry]



2. S. T. Peters, D. M. Schroeder, M. S. Haynes, D. Castelletti, and A. Romero-Wolf. Passive Synthetic Aperture Radar Imaging Using Radio-Astronomical Sources. IEEE Trans. Geosci. Remote Sens., pp 1-16, 2021. Keyword(s): Synthetic aperture radar, Passive radar, Sun, Radar, Focusing, Signal to noise ratio, Mathematical model, Passive radar, passive radio sounding, passive synthetic aperture radar (SAR), radio echo sounding, Back-Projection, Time-Domain Back-Projection, TDBP. [Abstract] [bibtex-entry]



3. F. Sica, G. Gobbi, Paola Rizzoli, and Lorrenzo Bruzzone. arphi-Net: Deep Residual Learning for InSAR Parameters Estimation. IEEE Transactions on Geoscience and Remote Sensing, pp 1-25, 2020. Keyword(s): Estimation, Synthetic aperture radar, Coherence, Noise reduction, Convolution, Measurement, Wavelet transforms, Coherence, convolutional neural network (CNN), deep learning (DL), denoising, interferometric phase, residual learning, synthetic aperture radar (SAR) interferometry.. [Abstract] [bibtex-entry]



4. R. Van Malderen, E. Pottiaux, A. Klos, P. Domonkos, M. Elias, T. Ning, O. Bock, J. Guijarro, F. Alshawaf, M. Hoseini, A. Quarello, E. Lebarbier, B. Chimani, V. Tornatore, S. Zengin Kazanci, and J. Bogusz. Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets. Earth and Space Science, 7(5), 2020. Note: Cited By 9. [bibtex-entry]



5. Marion Heublein, Fadwa Alshawaf, Bastian Erdnüss, Xiao Xiang Zhu, and Stefan Hinz. Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations. Journal of Geodesy, 93(2):197-217, 2019. Keyword(s): SAR Processing, Compressive Sensing, Tropospheric Path Delay, Synthetic Aperture Radar, Atmospheric Modelling, Atmospheric modeling, Meteorology, radar interferometry, synthetic aperture radar (SAR), SAR Tomography, GNSS, InSAR, Tropospheric Wet Path Delay. [Abstract] [bibtex-entry]



6. R. Jolivet and M. Simons. A Multipixel Time Series Analysis Method Accounting for Ground Motion, Atmospheric Noise, and Orbital Errors. Geophysical Research Letters, 45(4):1814-1824, 2018. Keyword(s): InSAR, Time Series. [Abstract] [bibtex-entry]



7. Ekaterina Tymofyeyeva and Yuri Fialko. Mitigation of atmospheric phase delays in InSAR data, with application to the eastern California shear zone. Journal of Geophysical Research: Solid Earth, 120(8):5952-5963, 2015. Note: 2015JB011886. Keyword(s): SAR Processing, Interferometry, SAR interferometry, differential SAR interferometry, DInSAR, Displacement, Surface Displacement, Atmosphere, APS, Transient deformation, Satellite geodesy: results, Satellite geodesy: technical issues, Seismic cycle related deformations, Integrations of techniques, InSAR, time series, atmospheric delays, transient deformation. [Abstract] [bibtex-entry]



8. Alireza Tabatabaeenejad, Mariko S. Burgin, and Mahta Moghaddam. Potential of L-Band Radar for Retrieval of Canopy and Subcanopy Parameters of Boreal Forests. IEEE Transactions on Geoscience and Remote Sensing, 50(6):2150-2160, June 2012. Keyword(s): calibration, data analysis, forestry, remote sensing by radar, simulated annealing, soil, vegetation mapping, AD 2010 06, Jet Propulsion Laboratory, L-band radar, National Aeronautics and Space Administration, SMAP mission, Uninhabited Aerial Vehicle Synthetic Aperture Radar, absolute retrieval error analysis, calibration method, canopy parameter retrieval, central Canada boreal forests, discrete scatterer radar model, forest structure, forward scattering model, global optimization scheme, inversion method, old black spruce site, old jack pine forests, optimization algorithm, optimization problem, relative retrieval error, scattering mechanisms, simulated annealing, soil moisture information, subcanopy parameter retrieval, surface soil moisture retrieval, synthetic data, vegetation parameters, young jack pine forests, Backscatter, Dielectric constant, L-band, Radar, Scattering, Soil moisture, Vegetation mapping, Allometric relationships, Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10), L-band radar, Soil Moisture Active and Passive (SMAP), boreal forest, canopy, inverse problem, microwave scattering. [Abstract] [bibtex-entry]



9. Tom R. Lauknes, Howard A. Zebker, and Y. Larsen. InSAR Deformation Time Series Using an L1-Norm Small-Baseline Approach. IEEE Trans. Geosci. Remote Sens., 49(1):536-546, January 2011. Keyword(s): SAR Processing, InSAR deformation time series, L1-norm small-baseline approach, displacement phase, land displacement monitoring, reweighted least squares algorithm, satellite data, satellite synthetic aperture radar interferometry, sparse data set, surface displacement, two-dimensional unwrapping, unwrapped interferogram, least squares approximations, radar interferometry, spaceborne radar, synthetic aperture radar, time series;. [Abstract] [bibtex-entry]



10. Ciro Cafforio, Claudio Prati, and Fabio Rocca. SAR Data Focusing Using Seismic Migration Techniques. IEEE Transactions on Aerospace and Electronic Systems, 27(2):194-207, March 1991. Keyword(s): SAR Processing, Range Migration Algorithm, omega-k, Wavenumber Domain Algorithm, Stationary Phase Method. [Abstract] [bibtex-entry]

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