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4 results on '"Jong-Sen Lee"'

1. Applying Polarimetric SAR Interferometric Data for Forest Classification

Author

Irena Hajnsek, M.R. Grunes, Laurent Ferro-Famil, Thomas L. Ainsworth, Kostas Papathanassiou, Jong-Sen Lee, Tobias Mette, Naval Aerospace Center (NRL), NRL, DLR Microwaves and Radar Institute, German Aerospace Center (DLR), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Nantes Université (NU)-Université de Rennes 1 (UR1)

Subjects
L band, Meteorology, Forest classification, 0211 other engineering and technologies, Classification tree analysis, 020206 networking & telecommunications, 02 engineering and technology, Polarimetric sar, Statistical classification, Interferometry, 0202 electrical engineering, electronic engineering, information engineering, Environmental science, 021101 geological & geomatics engineering, Remote sensing
Published
2005

2. Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier

Author

Laurent Ferro-Famil, Jong-Sen Lee, Eric Pottier, Institut d'Électronique et des Technologies du numéRique (IETR), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Naval Research Laboratory (NRL), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Université de Nantes (UN)-Université de Rennes 1 (UR1)

Subjects
Multivariate analysis, 010504 meteorology & atmospheric sciences, multivariate statistics, Computer science, Covariance matrix, 0211 other engineering and technologies, Image sensors, 02 engineering and technology, multifrequency SAR images, radar polarimetry, H/A/Alpha-Wishart classifier, 01 natural sciences, Wishart probability density function, Image analysis, Matrix (mathematics), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Probability density function, Computer vision, remote sensing by radar, polarimetric cross-correlation information, Radar scattering, Physics::Atmospheric and Oceanic Physics, ComputingMilieux_MISCELLANEOUS, dual frequency, Contextual image classification, polarimetric SAR, C-band SAR images, Statistics, Astrophysics::Instrumentation and Methods for Astrophysics, Conditional probability, Iterative algorithms, terrain mapping, Remote sensing, radar imaging, Electronic mail, terrain classification, classification scheme, cross-correlation matrix, France, NASA, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, SAR, synthetic aperture radar, Synthetic aperture radar, Wishart distribution, geophysical measurement technique, Iterative method, unsupervised classification, radar remote sensing, L-band SAR, Polarimetry, Nezer Forest, dual frequency polarimetric SAR data sets, Frequency conversion, Matrix decomposition, Multispectral pattern recognition, P-band SAR images, AIRSAR sensor, Radar imaging, Electrical and Electronic Engineering, Image sensor, coherency matrices, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Probability, scattering characteristics, Cross-correlation, Scattering, business.industry, maximum likelihood decision rule, land surface, polarimetric representation, Pattern recognition, cross-correlation, geophysical techniques, Frequency, coherency matrix, complex Wishart density function, multi-frequency radar, geophysical signal processing, decomposition theorem, Density functional theory, General Earth and Planetary Sciences, Artificial intelligence, Pixel, conditional probability, iterative adaptive classification, business, Laboratories, polarimetric SAR images, Scattering parameters, polarimetric coherency matrix, image classification
Abstract

Introduces a new classification scheme for dual frequency polarimetric SAR data sets. A (6/spl times/6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6/spl times/6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. These classification schemes are applied to full polarimetric P, L, and C-band SAR images of the Nezer Forest, France, acquired by the NASA/JPL AIRSAR sensor in 1989.

Published
2001

3. Unsupervised classification and analysis of natural scenes from polarimetric interferometric SAR data

Author

Laurent Ferro-Famil, Jong-Sen Lee, Eric Pottier, Institut d'Électronique et des Technologies du numéRique (IETR), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Naval Research Laboratory (NRL), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Université de Nantes (UN)-Université de Rennes 1 (UR1)

Subjects
Computer science, Entropy, 0211 other engineering and technologies, 02 engineering and technology, radar polarimetry, Wishart probability density function, Image analysis, InSAR, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Polarization, Interferometric synthetic aperture radar, 0202 electrical engineering, electronic engineering, information engineering, Astrophysics::Solar and Stellar Astrophysics, Computer vision, remote sensing by radar, Radar scattering, Entropy (energy dispersal), Physics::Atmospheric and Oceanic Physics, ComputingMilieux_MISCELLANEOUS, Eigenvalues and eigenfunctions, Contextual image classification, Entropy (statistical thermodynamics), Astrophysics::Instrumentation and Methods for Astrophysics, Conditional probability, terrain mapping, Remote sensing, Adaptive filter, radar imaging, Interferometry, natural scene, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, synthetic aperture radar, Wishart distribution, Synthetic aperture radar, geophysical measurement technique, unsupervised classification, Layout, radar remote sensing, Probability density function, polarimetric interferometric SAR, Matrix decomposition, Radar imaging, Entropy (information theory), 021101 geological & geomatics engineering, adaptive signal processing, business.industry, maximum likelihood decision rule, land surface, Anisotropic magnetoresistance, polarimetric representation, 020206 networking & telecommunications, Pattern recognition, geophysical techniques, geophysical signal processing, Artificial intelligence, iterative adaptive classification, business, Scattering parameters, interferometric polarimetric coherency matrix, image classification
Abstract

In this paper is introduced a classification approach for polarimetric interferometric SAR data sets, based on the analysis of an interferometric (6/spl times/6) polarimetric coherency matrix properties. From the Wishart probability density function of this polarimetric representation, is defined a maximum likelihood decision rule to perform an iterative adaptive classification. Another classification scheme based on the derivation of the conditional probability of the cross-correlation between both data sets is presented.

Published
2001

4. Retrievals of land surface parameters from measured brightness temperatures at 1.4 and 10.65 ghz by a neural network

Author

W.-J. Wang, Yuei-An Liou, Jean-Pierre Wigneron, S.-F. Liu, Jong-Sen Lee, ProdInra, Migration, National Central University, Oriental Institute of Technology, Partenaires INRAE, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), and Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDE] Environmental Sciences, Brightness, L band, Propagation of uncertainty, Radiometer, Moisture, [SDV]Life Sciences [q-bio], 0211 other engineering and technologies, 04 agricultural and veterinary sciences, 02 engineering and technology, microwaves, Data set, [SDV] Life Sciences [q-bio], [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Radiometry, Environmental science, soil moisture, Water content, 021101 geological & geomatics engineering, Remote sensing
Abstract

International audience; Field measurements are utilized to examine the sensitivity of H- and V-polarized radiometric signatures at 1.4 (L-band), 10.65 (X-band) GHz, and their combinations in terms of viewing angles and frequencies to the wheat biomass and soil moisture through an Error Propagation Learning Back Propagation (EPLBP) neural network. The radiometric measurements were taken by the PORTOS radiometer over whe at fields through 3 month growth cycles in 1993 (PORTOS-93) and 1996 (PORTOS-96). Soil moisture content (SMC) profiles were measured into depth of 10 cm in 1993, and 5 cm in 1996. The wheat was sampled daily in 1993 and twice a week in 1996 to obtain its dry and wet biomass. The EPLBP neural network is trained with observations randomly chosen from the PORTOS-93 data, and evaluated by the rest of the very same data set. Then, the trained neural network is further evaluated based on the PORTOS-96 data set for practical concern. During both field campaigns, the L-band radiometer measured brightness temperatures at a wide range of viewing angles from 0 to 50 degrees, while the X-band radiometer was operated at an incident angle of 50 degrees.

Published
2001

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