Publications about 'training'

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. Francescopaolo Sica, Andrea Pulella, Matteo Nannini, Muriel Pinheiro, and Paola Rizzoli. Repeat-pass SAR interferometry for land cover classification: A methodology using Sentinel-1 Short-Time-Series. Remote Sensing of Environment, 232:111277, 2019. Keyword(s): Land cover classification, SAR, Interferometric coherence, Sentinel-1, Temporal decorrelation. [Abstract] [bibtex-entry]



3. 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]



4. K. Landmark, A. H. Schistad Solberg, A. Austeng, and Roy E. Hansen. Bayesian Seabed Classification Using Angle-Dependent Backscatter Data From Multibeam Echo Sounders. IEEE Journal of Oceanic Engineering, 39(4):724-739, October 2014. Keyword(s): Synthetic Aperture Sonar, SAS, Bayes methods, Gaussian processes, acoustic wave scattering, approximation theory, backscatter, oceanographic techniques, pattern classification, piecewise constant techniques, sonar, statistical analysis, Bayesian seabed classification, Gaussian statistical model, North Sea data set, acoustical seabed classification, across-track spatial resolution, angle-dependent backscatter data, intrinsic scattering strength statistics, mapping seabed sediment, multibeam echo sounder, multibeam sonar data processing, piecewise constant function, piecewise function approximation, seabed scattering strength, spatial averaging, standard Bayesian theory, Bayes methods, Classification algorithms, Remote sensing, Sea floor, Sediments, Sonar, Underwater acoustics, Bayesian methods, classification algorithms, remote sensing, seafloor, sediments, sonar. [Abstract] [bibtex-entry]



5. Ross F. Nelson, Peter Hyde, Patrick Johnson, Bomono Emessiene, Marc L. Imhoff, Robert Campbell, and Wilson Edwards. Investigating RaDAR-LiDAR synergy in a North Carolina pine forest. Remote Sensing of Environment, 110(1):98-108, September 2007. Keyword(s): SAR Processing, Biomass, Forest, VHFRaDAR, profiling LiDAR, biomass, RaDAR-LiDAR synergy, VHFSAR DATA, SMALL-FOOTPRINT LIDAR, AIRBORNE LASER DATA, STEM VOLUME, STAND CHARACTERISTICS, AERIAL-PHOTOGRAPHY, VEGETATION BIOMASS, CONIFEROUS FOREST, BOREAL FORESTS, SCANNER DATA. [Abstract] [bibtex-entry]



6. Jong-Sen Lee, M.R. Grunes, T.L. Ainsworth, Li-Jen Du, D.L. Schuler, and Shane R. Cloude. Unsupervised classification using polarimetric decomposition and the complex Wishart classifier. IEEE Transactions on Geoscience and Remote Sensing, 37(5):2249-2258, September 1999. Keyword(s): geophysical signal processing, geophysical techniques, image classification, radar imaging, radar polarimetry, remote sensing by radar, synthetic aperture radar, terrain mappingSAR, complex Wishart classifier, complex Wishart distribution, covariance matrix, entropy-alpha plane, geophysical measurement technique, initial classification map, iteration, land surface, man-made object, maximum likelihood classifier, polarimetric decomposition, polarimetric target decomposition, polarization, radar remote sensing, terrain mapping, terrain type, training, unsupervised classification. [Abstract] [bibtex-entry]



7. J. S. Lee, M. R. Grunes, and Ron Kwok. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. International Journal of Remote Sensing, 15(11):2299-2311, 1994. [Abstract] [bibtex-entry]

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This collection of SAR literature is far from being complete.
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