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Dictionary-based probability density function estimation for high-resolution SAR data

Vladimir Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia, Charles A. Bouman, Eric L. Miller, Ilya Pollak
2009 Computational Imaging VII  
In this work, we develop a parametric finite mixture model for the statistics of pixel intensities in high resolution synthetic aperture radar (SAR) images.  ...  The experimental results with a set of several high resolution COSMO-SkyMed images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms  ...  The authors would also like to thank the Italian Space Agency (ASI) for providing the COSMO-SkyMed images of Piemonte ( c ASI, 2008) and CNES for providing the RAMSES image ( c ONERA-CNES, 2004).  ... 
doi:10.1117/12.816102 dblp:conf/cimaging/KrylovMSZ09 fatcat:qgh4pduk7fep7a7iulfnz7fo5u

Unsupervised Classification of SAR Images Using Hierarchical Agglomeration and EM [chapter]

Koray Kayabol, Vladimir A. Krylov, Josiane Zerubia
2012 Lecture Notes in Computer Science  
We exploit amplitude statistics in a Finite Mixture Model (FMM), and a Multinomial Logistic (MnL) latent class label model for a mixture density to obtain spatially smooth class segments.  ...  We implement an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images. The foundation of algorithm is based on Classification Expectation-Maximization (CEM).  ...  The authors would like to thank Aurélie Voisin (Ariana INRIA, France) for interesting discussions and Astrium-Infoterra GmbH for providing the TerraSAR-X image.  ... 
doi:10.1007/978-3-642-32436-9_5 fatcat:ectnw3gwhjdppalnel3lfft4hy

High resolution SAR-image classification by Markov random fields and finite mixtures

Gabriele Moser, Vladimir Krylov, Sebastiano B. Serpico, Josiane Zerubia, Charles A. Bouman, Ilya Pollak, Patrick J. Wolfe
2010 Computational Imaging VIII  
In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic aperture radar (SAR) amplitude images.  ...  This approach combines the Markov random field model to Bayesian image classification and a finite mixture technique for probability density function estimation.  ...  The authors would like to thank the Italian Space Agency (ASI) for providing the COSMO-SkyMed image of Piemonte ( c ASI, 2008).  ... 
doi:10.1117/12.838594 dblp:conf/cimaging/MoserKSZ10 fatcat:u5fgpub4f5ajpoyloih5mnljwu

SAR image classification with non-stationary Multinomial Logistic mixture of amplitude and texture densities

Koray Kayabol, Aurelie Voisin, Josiane Zerubia
2011 2011 18th IEEE International Conference on Image Processing  
We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose.  ...  Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments.  ...  The TerraSAR-X image of Rosenheim ( c Infoterra) was obtained from http://www.infoterra.de/.  ... 
doi:10.1109/icip.2011.6115784 dblp:conf/icip/KayabolVZ11 fatcat:ehllui7yvjc4vcuk5zgy7w3rhu

Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas, and Markov random fields using textural features

Aurélie Voisin, Gabriele Moser, Vladimir A. Krylov, Sebastiano B. Serpico, Josiane Zerubia, Lorenzo Bruzzone
2010 Image and Signal Processing for Remote Sensing XVI  
The proposed supervised method combines a finite mixture technique to estimate class-conditional probability density functions, Bayesian classification, and Markov random fields (MRFs).  ...  sification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features. Lorenzo Bruzzone.  ...  The accuracy of the proposed algorithm was validated in the application to urban/land/water separation on several single-pol high resolution COSMO-SkyMed images.  ... 
doi:10.1117/12.865023 fatcat:d5j33fsiezdsnjpugf3qkzkyvq

A comparison of texture and amplitude based unsupervised SAR image classifications for urban area extraction

Koray Kayabol, Josiane Zerubia
2012 2012 IEEE International Geoscience and Remote Sensing Symposium  
We compare the performance of the texture and the amplitude based mixture density models for urban area extraction from high resolution Synthetic Aperture Radar (SAR) images.  ...  We exploit a Multinomial Logistic (MnL) latent class label model as a mixture density to obtain spatially smooth class segments.  ...  Finite Mixture Model (FMM) is a suitable statistical model to represent SAR image histogram and to perform a model based classification [1] , [2] .  ... 
doi:10.1109/igarss.2012.6350519 dblp:conf/igarss/KayabolZ12 fatcat:ntfgx6obzfbopme3numt2hrskm

Unsupervised Amplitude and Texture Classification of SAR Images With Multinomial Latent Model

Koray Kayabol, Josiane Zerubia
2013 IEEE Transactions on Image Processing  
In a finite mixture model, we bring together the Nakagami densities to model the class amplitudes and a 2D Auto-Regressive texture model with t-distributed regression error to model the textures of the  ...  Abstract-We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images for modelbased classification purpose.  ...  several interesting discussions, Ismail Ben Ayed for providing MLS algorithm [67] , [68] online and the Italian Space Agency (ASI) for providing the COSMO-SkyMed images.  ... 
doi:10.1109/tip.2012.2219545 pmid:23008256 fatcat:dmlhqvaw3ram7lxvuk4cpms3hm

Oil Spill Detection in SAR Images Using Online Extended Variational Learning of Dirichlet Process Mixtures of Gamma Distributions

Ahmed Almulihi, Fahd Alharithi, Sami Bourouis, Roobaea Alroobaea, Yogesh Pawar, Nizar Bouguila
2021 Remote Sensing  
In this paper, we propose a Dirichlet process (DP) mixture model of Gamma distributions, which is an extension of the finite Gamma mixture model to the infinite case.  ...  We demonstrated the performance and merits of the proposed statistical framework with a challenging real-world application namely oil spill detection in synthetic aperture radar (SAR) images.  ...  These works rely on highly trained human operators to asses and verify each region in a given SAR image.  ... 
doi:10.3390/rs13152991 fatcat:oe5ocjropzfyllds4hy3eb4ewe

Supervised High-Resolution Dual-Polarization SAR Image Classification by Finite Mixtures and Copulas

Vladimir A. Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia
2011 IEEE Journal on Selected Topics in Signal Processing  
In this paper a novel supervised classification approach is proposed for high resolution dual polarization (dualpol) amplitude satellite synthetic aperture radar (SAR) images.  ...  A novel probability density function (pdf) model of the dual-pol SAR data is developed that combines finite mixture modeling for marginal probability density functions estimation and copulas for multivariate  ...  The TerraSAR-X image of Sanchagang was taken from http://www.infoterra.de/, where it is available for free testing ( c Infoterra GmbH, 2008).  ... 
doi:10.1109/jstsp.2010.2103925 fatcat:2wmtsvj3lfd63glh4jtd5gt6iq

Supervised Classification of Multisensor and Multiresolution Remote Sensing Images With a Hierarchical Copula-Based Approach

Aurelie Voisin, Vladimir A. Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia
2014 IEEE Transactions on Geoscience and Remote Sensing  
In this paper we develop a novel classification approach for multi-resolution, multi-sensor (optical and synthetic aperture radar, SAR) and/or multi-band images.  ...  Such copulas combine the class-conditional marginal probability density functions of each input channel that are estimated by finite mixtures of well-chosen parametric families.  ...  All Rights Reserved) in the framework of the project "Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures (2010-  ... 
doi:10.1109/tgrs.2013.2272581 fatcat:ijd3q22nwbfhfck3jhwf224zje

Multichannel hierarchical image classification using multivariate copulas

Aurélie Voisin, Vladimir A. Krylov, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia, Charles A. Bouman, Ilya Pollak, Patrick J. Wolfe
2012 Computational Imaging X  
For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families.  ...  For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions.  ...  Moreover, the use of finite mixtures can be seen as a generalization of the determination of a single PDF, and allows to estimate both the best finite mixture model and/or the best single PDF model.  ... 
doi:10.1117/12.917298 dblp:conf/cimaging/VoisinKMSZ12 fatcat:ecwngwjugrgg3bb4khrgzigxou

Synthetic aperture radar image classification via mixture approaches

Vladimir A. Krylov, Josiane Zerubia
2011 2011 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS 2011)  
In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification.  ...  To address it we consider a statistical finite mixture approach to probability density function estimation.  ...  Serpico (Univ. of Genoa, Italy) for their helpful comments and assistance in preparing this paper.  ... 
doi:10.1109/comcas.2011.6105807 fatcat:qn5fhxsq4naghgfdeo4dyhm5pu

Probability Density Function Estimation for Classification of High-Resolution SAR Images [chapter]

Vladimir Krylov, Gabriele Moser, Sebastiano Serpico, Josiane Zerubia
2012 Signal and Image Processing for Remote Sensing, Second Edition  
It addressed the problem by adopting a finite mixture model [12] for the SAR amplitude pdf, i.e., by postulating the unknown amplitude pdf to be a linear combination of parametric components, each corresponding  ...  The classification technique developed in this chapter combines the Markov random field (MRF) approach to Bayesian image classification with the finite mixture EDSEM amplitude pdf estimator.  ...  To take into account possible heterogenous scenarios, when several distinct land-cover typologies are present in the same SAR image, a finite mixture model (FMM) [12] for the distribution of grey levels  ... 
doi:10.1201/b11656-20 fatcat:v7ryujp55jhbfncdlxbbx2dkwe

Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields

R. Fjortoft, Y. Delignon, W. Pieczynski, M. Sigelle, F. Tupin
2003 IEEE Transactions on Geoscience and Remote Sensing  
This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation.  ...  Hidden Markov chain models, applied to a Hilbert-Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems  ...  In generalized mixture estimation, we are not limited to Gaussian distributions, but to a finite set of distribution families [14] , [21] .  ... 
doi:10.1109/tgrs.2003.809940 fatcat:a4bib35cvza57daq7fz2jf5ax4

A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery

Filsa Bioresita, Anne Puissant, André Stumpf, Jean-Philippe Malet
2018 Remote Sensing  
Thus, in this paper, we propose a fully automatic processing chain for rapid flood and surface water mapping with smooth labeling based on Sentinel-1 amplitude data.  ...  Sentinel-1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to facilitate the monitoring of surface water changes, which are dynamic in space and time.  ...  It is a continuation of the various works carried out at LIVE and EOST on satellite image time series processing for the analysis of environmental processes.  ... 
doi:10.3390/rs10020217 fatcat:t2shi7dvkfhnxgtkmdcl6ghwii
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