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Publications about 'root-mean-square error'

Articles in journal or book chapters

  1. Emanuele Santi, Marco Brogioni, Marion Leduc-Leballeur, Giovanni Macelloni, Francesco Montomoli, Paolo Pampaloni, Juha Lemmetyinen, Juval Cohen, Helmut Rott, Thomas Nagler, Chris Derksen, Joshua King, Nick Rutter, Richard Essery, Cecile Menard, Melody Sandells, and Michael Kern. Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X- and Ku-Bands. IEEE Transactions on Geoscience and Remote Sensing, pp 1-16, 2021. Keyword(s): SAR Processing, Artificial neural networks (ANNs), dense medium radiative transfer (DMRT), quasi Mie scattering (QMS) model, snow depth (SD), snow water equivalent (SWE), SnowSAR, synthetic aperture radar, SAR. [Abstract] [bibtex-entry]


  2. G. H. X. Shiroma and M. Lavalle. Digital Terrain, Surface, and Canopy Height Models From InSAR Backscatter-Height Histograms. IEEE Transactions on Geoscience and Remote Sensing, 58(6):3754-3777, June 2020. Keyword(s): backscatter, forestry, optical radar, radar imaging, radar interferometry, radar polarimetry, remote sensing by radar, synthetic aperture radar, vegetation, vegetation mapping, digital terrain, canopy height models, InSAR backscatter-height histogram, interferometric synthetic aperture radar backscatter-height histograms, single-look backscatter measurements, InSAR phase height, InSAR histogram, LiDAR waveforms, ground topography, full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar data, forest height, Histograms, Laser radar, Forestry, Vegetation mapping, Backscatter, Synthetic aperture radar, Digital elevation models (DEMs), forest height, interferometry, L-band, polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR), polarimetry, SAR. [Abstract] [bibtex-entry]


  3. Franziska Koch, Patrick Henkel, Florian Appel, Lino Schmid, Heike Bach, Markus Lamm, Monika Prasch, J�rg Schweizer, and Wolfram Mauser. Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay. Water Resources Research, 55(5):4465-4487, 2019. Keyword(s): GNSS, snow water equivalent, liquid water content, snow cover, SnowSense, GNSS signals, Global Positioning System. [Abstract] [bibtex-entry]


  4. J. Pan, M. Durand, M. Sandells, J. Lemmetyinen, E. J. Kim, J. Pulliainen, A. Kontu, and C. Derksen. Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation. IEEE Transactions on Geoscience and Remote Sensing, 54(4):2001-2019, April 2016. Keyword(s): radiative transfer, remote sensing, snow, HUT snow emission model, Helsinki University of Technology, brightness temperature simulation, snow water equivalent retrieval algorithm, passive microwave measurement, multiple-layer HUT model, Microwave Emission Model of Layered Snowpacks, scattered intensity, radiative transfer equation, one-flux equation, two-flux theory, HUT scattering coefficient, trapped-radiation, natural snow cover, Sodankyla, Finland, Churchill, Canada, Colorado, USA, snow grain size was, deep snow, Born approximation, root-mean-square error, Snow, Mathematical model, Scattering, Grain size, Microwave theory and techniques, Ice, Correlation, Model comparison, passive microwave remote sensing, snow, Model comparison, passive microwave remote sensing, snow. [Abstract] [bibtex-entry]


  5. Astor T. Caicoya, Matteo Pardini, Irena Hajnsek, and Konstantinos P. Papathanassiou. Forest Above-Ground Biomass Estimation From Vertical Reflectivity Profiles at L-Band. IEEE Geosci. Remote Sens. Lett., 12(12):2379-2383, December 2015. Keyword(s): SAR Processing, SAR Tomography, L-band, Capon, forestry, vegetation mapping, L-band reflectivity profiles, SAR tomography, forest above-ground biomass estimation, forest stand densities, height measurements, root-mean-square error, vertical forest structure information, vertical radar reflectivity profiles, vertical reflectivity profiles, Biomass, Estimation, Image color analysis, L-band, Remote sensing, Synthetic aperture radar, Forest allometry, L-band, forest biomass, synthetic aperture radar (SAR) tomography, vertical forest structure, vertical reflectivity profiles. [Abstract] [bibtex-entry]


  6. Maciej J. Soja, H.J. Persson, and Lars M.H. Ulander. Estimation of Forest Biomass From Two-Level Model Inversion of Single-Pass InSAR Data. IEEE Trans. Geosci. Remote Sens., 53(9):5083-5099, September 2015. Keyword(s): data acquisition, digital elevation models, forestry, radar interferometry, remote sensing by radar, synthetic aperture radar, vegetation, AD 2008, AD 2010, AD 2011, AD 2012, AD 2013, InSAR processing, Krycklan feature, Remningstorp feature, Swedish test site, VV-polarized TanDEM-X acquisition, aboveground biomass estimation, biomass predictor, canopy density, digital terrain model, forest biomass estimation, forest height, hemiboreal forest, northern Sweden, single-pass InSAR data, single-pass interferometric synthetic aperture radar data, southern Sweden, two-level model inversion, Biological system modeling, Biomass, Computational modeling, Correlation, Decorrelation, Estimation, Synthetic aperture radar, Aboveground biomass (AGB), TanDEM-X (TDM), canopy density, forest height, interferometric model, interferometric syntheticaperture radar (InSAR), two-level model (TLM). [Abstract] [bibtex-entry]


  7. Dochul Yang and Sean M. Buckley. Estimating High-Resolution Atmospheric Phase Screens From Radar Interferometry Data. IEEE Transactions on Geoscience and Remote Sensing, 49(8):3117 -3128, August 2011. Keyword(s): SAR Processing, Persistent Scatterer Interferometry, PSI, Arizona, HiRAPS algorithm, InSAR deformation measurement, Phoenix, Radarsat-1 image, USA, bubbles, deformation time series, height error difference, high-resolution atmospheric phase screen estimation, high-spatial-frequency atmospheric effects, multiinterferogram phase correlation, nonlinear deformation, persistent scatterer algorithm, pixel density, radar interferometry data, root mean square error, satellite-orbit repeat cycle, short-period interferogram, singular value decomposition, spatiotemporal filtering, time series algorithm, time-linear deformation, topographic phase contribution, geophysical image processing, geophysical techniques, mean square error methods, radar imaging, radar interferometry, remote sensing by radar, singular value decomposition, synthetic aperture radar, time series;. [Abstract] [bibtex-entry]


  8. Maxim Neumann, Laurent Ferro-Famil, and Andreas Reigber. Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric SAR Data. IEEE Trans. Geosci. Remote Sens., 48(3):1086-1104, March 2010. Keyword(s): SAR Processing, Multibaseline SAR, Germany, PolInSAR, RVoG, vertical structure, Traunstein test site, airborne SAR, L-band, angular distribution, canopy layer heights, differential extinction, double-bounce ground-trunk interactions, forest layer heights, forest parameter retrieval, forest structure estimation, forest vegetation, ground topography, ground-to-volume ratio, ground-truth measurements, interferometric coherence, particle scattering anisotropy, polarimetric Synthetic Aperture Radar interferometry, polarimetric decomposition, polarimetric scattering media model, polarization orientation randomness, random-volume-over-ground PolInSAR parameter inversion, repeat-pass configuration, root-mean-square error, surface scattering, temporal decorrelation, tree morphology, volume coherency matrices, volumetric canopy, volumetric understory scattering, wave attenuation, radar interferometry, radar polarimetry, remote sensing by radar, synthetic aperture radar, vegetation mapping;. [Abstract] [bibtex-entry]


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This collection of SAR literature is far from being complete.
It is rather a collection of papers which I store in my literature data base. Hence, the list of publications under PUBLICATIONS OF AUTHOR'S NAME should NOT be mistaken for a complete bibliography of that author.




Last modified: Fri Feb 24 14:24:45 2023
Author: Othmar Frey, Earth Observation and Remote Sensing, Institute of Environmental Engineering, Swiss Federal Institute of Technology - ETH Zurich .


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