Competence Centre on Information Extraction and Image Understanding for Earth Observation Matteo Soccorsi (1) and Mihai Datcu (1,2) A Complex GMRF for.

Slides:

Advertisements

Similar presentations

Applications of one-class classification

Advertisements

J. Daunizeau Institute of Empirical Research in Economics, Zurich, Switzerland Brain and Spine Institute, Paris, France Bayesian inference.

Bayesian Belief Propagation

On Estimation of Soil Moisture & Snow Properties with SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa.

MEG/EEG Inverse problem and solutions In a Bayesian Framework EEG/MEG SPM course, Bruxelles, 2011 Jérémie Mattout Lyon Neuroscience Research Centre ? ?

Multi-Task Compressive Sensing with Dirichlet Process Priors Yuting Qi 1, Dehong Liu 1, David Dunson 2, and Lawrence Carin 1 1 Department of Electrical.

What is Statistical Modeling

Folie 1 Ambiguity Suppression by Azimuth Phase Coding in Multichannel SAR Systems DLR - Institut für Hochfrequenztechnik und Radarsysteme F. Bordoni, M.

Uncertainty Representation. Gaussian Distribution variance Standard deviation.

Remote Sensing Technology Institute Extraction of the surface velocity of rivers with SAR- ATI H. Runge 1, S. Suchandt 1, R. Horn 2, T. Eiglsperger 3 German.

1 Unsupervised Learning With Non-ignorable Missing Data Machine Learning Group Talk University of Toronto Monday Oct 4, 2004 Ben Marlin Sam Roweis Rich.

Relevance Feedback based on Parameter Estimation of Target Distribution K. C. Sia and Irwin King Department of Computer Science & Engineering The Chinese.

Inversion of Z-Axis Tipper Electromagnetic (Z-TEM)‏ Data The UBC Geophysical Inversion Facility Elliot Holtham and Douglas Oldenburg.

Machine Learning CMPT 726 Simon Fraser University

Collaborative Ordinal Regression Shipeng Yu Joint work with Kai Yu, Volker Tresp and Hans-Peter Kriegel University of Munich, Germany Siemens Corporate.

CHAPTER 4: Parametric Methods. Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 2 Parametric Estimation Given.

7-1 Introduction The field of statistical inference consists of those methods used to make decisions or to draw conclusions about a population. These.

Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I Povo, Trento, Italy Remote.

DLR German Aerospace Center  Research Institution  Space Agency  Project Management Agency  32 institutes and facilities  16 sites all.

EE513 Audio Signals and Systems Statistical Pattern Classification Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.

Overview G. Jogesh Babu. Probability theory Probability is all about flip of a coin Conditional probability & Bayes theorem (Bayesian analysis) Expectation,

earthobs.nr.no Land cover classification of cloud- and snow-contaminated multi-temporal high-resolution satellite images Arnt-Børre Salberg and.

Efficient design of a C-band aperture-coupled stacked microstrip array using Nexxim and Designer Alberto Di Maria German Aerospace Centre (DLR) – Microwaves.

EADS DS / SDC LTIS Page 1 7 th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen.

Blue: Histogram of normalised deviation from “true” value; Red: Gaussian fit to histogram Presented at ESA Hyperspectral Workshop 2010, March 16-19, Frascati,

Dr A VENGADARAJAN, Sc ‘F’, LRDE

PREFER 1 st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy PREFER WP 3.2 Information support to Recovery/Reconstruction Task 7 Damage Severity Map PREFER.

A Comparison Between Bayesian Networks and Generalized Linear Models in the Indoor/Outdoor Scene Classification Problem.

GISMO Simulation Study Objective Key instrument and geometry parameters Surface and base DEMs Ice mass reflection and refraction modeling Algorithms used.

Competence Centre on Information Extraction and Image Understanding for Earth Observation 29/03/07 Blind city classification using aggregation of clusterings.

7-1 Introduction The field of statistical inference consists of those methods used to make decisions or to draw conclusions about a population. These.

Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models Mike West Computing Science and Statistics, Vol. 24, pp , 1993.

Forward-Scan Sonar Tomographic Reconstruction PHD Filter Multiple Target Tracking Bayesian Multiple Target Tracking in Forward Scan Sonar.

Competence Centre on Information Extraction and Image Understanding for Earth Observation Prof. Dr. Mihai Datcu SATELLITE IMAGE ARTIFACTS DETECTION BASED.

Authors: Rupert Paget, John Homer, and David Crisp

1 ON THE EXTENSION OF THE PRODUCT MODEL IN POLSAR PROCESSING FOR UNSUPERVISED CLASSIFICATION USING INFORMATION GEOMETRY OF COVARIANCE MATRICES P. Formont.

Digital Image Processing

SCENE CLASS RECOGNITION USING HIGH RESOLUTION SAR/INSAR SPECTRAL DECOMPOSITION METHODS Anca Popescu, Inge Gavat University Politehnica Bucharest (UPB)

Competence Centre on Information Extraction and Image Understanding for Earth Observation 1 High Resolution Complex SAR Image Analysis Using Azimuth Sub-Band.

Competence Centre on Information Extraction and Image Understanding for Earth Observation 29th March 2007 Category - based Semantic Search Engine 1 Mihai.

Chapter 3: Maximum-Likelihood Parameter Estimation l Introduction l Maximum-Likelihood Estimation l Multivariate Case: unknown , known  l Univariate.

PARALLEL FREQUENCY RADAR VIA COMPRESSIVE SENSING

University of Electronic Science and Technology of China

1/18 New Feature Presentation of Transition Probability Matrix for Image Tampering Detection Luyi Chen 1 Shilin Wang 2 Shenghong Li 1 Jianhua Li 1 1 Department.

Lecture 2: Statistical learning primer for biologists

ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.

1 Chapter 8: Model Inference and Averaging Presented by Hui Fang.

Discriminative Training and Machine Learning Approaches Machine Learning Lab, Dept. of CSIE, NCKU Chih-Pin Liao.

Lecture 5: Statistical Methods for Classification CAP 5415: Computer Vision Fall 2006.

Statistical Methods. 2 Concepts and Notations Sample unit – the basic landscape unit at which we wish to establish the presence/absence of the species.

Edge Preserving Spatially Varying Mixtures for Image Segmentation Giorgos Sfikas, Christophoros Nikou, Nikolaos Galatsanos (CVPR 2008) Presented by Lihan.

Ch 1. Introduction Pattern Recognition and Machine Learning, C. M. Bishop, Updated by J.-H. Eom (2 nd round revision) Summarized by K.-I.

ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.

DISPLACED PHASE CENTER ANTENNA SAR IMAGING BASED ON COMPRESSED SENSING Yueguan Lin 1,2,3, Bingchen Zhang 1,2, Wen Hong 1,2 and Yirong Wu 1,2 1 National.

Overview G. Jogesh Babu. R Programming environment Introduction to R programming language R is an integrated suite of software facilities for data manipulation,

Design and Use of Earth Observation Image Content Tools Mihai Datcu(1, 2), Daniele Cerra(1), Houda Chaabouni-Chouayakh(1), Amaia de Miguel(1), Daniela.

Meng Liu,Hong Zhang,Chao Wang, Bo Zhang

Bag-of-Visual-Words Based Feature Extraction

Impact of SAR data filtering on crop classification accuracy

7-1 Introduction The field of statistical inference consists of those methods used to make decisions or to draw conclusions about a population. These.

CH 5: Multivariate Methods

Department of Civil and Environmental Engineering

Propagating Uncertainty In POMDP Value Iteration with Gaussian Process

Shashi Shekhar Weili Wu Sanjay Chawla Ranga Raju Vatsavai

EE513 Audio Signals and Systems

Parametric Methods Berlin Chen, 2005 References:

Bayesian inference J. Daunizeau

Ch 3. Linear Models for Regression (2/2) Pattern Recognition and Machine Learning, C. M. Bishop, Previously summarized by Yung-Kyun Noh Updated.

Multi-Information Based GCPs Selection Method

Presentation transcript:

Competence Centre on Information Extraction and Image Understanding for Earth Observation Matteo Soccorsi (1) and Mihai Datcu (1,2) A Complex GMRF for SAR Image Analysis: A Bayesian Approach (1) German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Photogrammetry and Image Analysis (PB) (2) École Nationale Supérieure de Télécomunication, Paris, France 7 th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Images DLR, Oberpfaffenhofen, 28 th to 30 th of March, 2007

Competence Centre on Information Extraction and Image Understanding for Earth Observation Outline  High Resolution (HR) Synthetic Aperture Radar (SAR) images  Image model: complex Gauss-Markov Random Fields (GMRF)  Bayesian frame  Case study  Evidence maximization information extraction from detected images  Classification Comparison  Conclusion

Competence Centre on Information Extraction and Image Understanding for Earth Observation Outline  HR SAR images  Image model: complex GMRF  Bayesian frame  Case study  Evidence maximization information extraction from detected images  Classification Comparison  Conclusion

Competence Centre on Information Extraction and Image Understanding for Earth Observation High Resolution SAR Images 1/3  Complex data (E-SAR X band, Dresden):  Image interpretation is a difficult task. Amplitude Phase Real channel Imaginary channel

Competence Centre on Information Extraction and Image Understanding for Earth Observation High Resolution SAR Images 2/3  Examples of different textures:  High resolution SAR images show different phase behavior.

Competence Centre on Information Extraction and Image Understanding for Earth Observation High Resolution SAR Images 3/3  Example of structured target with correlated phase (E-SAR X band, DLR area):

Competence Centre on Information Extraction and Image Understanding for Earth Observation General Concept  There is information in the phase of HR SAR data;  It is important to exploit this information for better scene understanding;  The task is to model complex data with phase correlation pattern;  We assume the data to be modeled by GMRF;  We extend the definition of real GMRF to complex domain.

Competence Centre on Information Extraction and Image Understanding for Earth Observation Outline  HR SAR images  Image model: complex GMRF  Bayesian frame  Case study  Evidence maximization information extraction from detected images  Classification Comparison  Conclusion

Competence Centre on Information Extraction and Image Understanding for Earth Observation Image Model: Complex GMRF  GMRF model is characterized by the following conditional distribution: Neighborhood

Competence Centre on Information Extraction and Image Understanding for Earth Observation GMRF Concept

Competence Centre on Information Extraction and Image Understanding for Earth Observation Simulation of Complex GMRF  Phase image examples:  By varying the number of the parameters and their values we can model different kinds of textures. Model order 1Model order 2Model order 6 Model order 3Model order 5Model order 8

Competence Centre on Information Extraction and Image Understanding for Earth Observation Outline  HR SAR images  Image model: complex GMRF  Bayesian frame  Case study  Evidence maximization information extraction from detected images  Classification Comparison  Conclusion

Competence Centre on Information Extraction and Image Understanding for Earth Observation Model Fitting  In the model fitting we apply Bayes’ rule to maximize the probability distribution of the parameter θ given the likelihood of the observation x s and the prior of the parameter:  The evidence p( x s |H i ) is neglected at this level of inference (because it is a constant factor) and the equation of the MAP estimate is:

Competence Centre on Information Extraction and Image Understanding for Earth Observation Model selection  We find the most plausible model H i out of a set of existing models {H j } given the image data x s :  The task is obtained through selecting the model which maximize the evidence obtained by marginalization:  Where the integral is over the multidimensional parameters space and p(θ |H i ) is the prior of the parameters.

Competence Centre on Information Extraction and Image Understanding for Earth Observation Outline  HR SAR images  Image model: complex GMRF  Bayesian frame  Case study  Evidence maximization information extraction from detected images  Classification Comparison  Conclusion

Competence Centre on Information Extraction and Image Understanding for Earth Observation Parameter Estimation  Block diagram of the algorithm:  The number of the output parameters depends on the model order complexity. Clique Matrix MAP Estimate G

Competence Centre on Information Extraction and Image Understanding for Earth Observation Classification Results  We processed and classified an E-SAR scene of Dresden city, Germany. Azimuth resolution 0.72 m, range resolution 1.99 m, covering an area of about 5.2x2.0 Km 2

Competence Centre on Information Extraction and Image Understanding for Earth Observation Parameter Estimation for Model Order Selection  We chose three classes and performed the model order selection by evidence computation:

Competence Centre on Information Extraction and Image Understanding for Earth Observation Texture Feature 1/2 Amplitude Phase Variance Vertical clique (real part) Horizontal clique (real part) Evidence Vertical clique (imaginary part) Horizontal clique (imaginary part)

Competence Centre on Information Extraction and Image Understanding for Earth Observation Texture Feature 2/2 Amplitude Phase Variance Vertical clique (real part) Horizontal clique (real part) Evidence Vertical clique (imaginary part) Horizontal clique (imaginary part)

Competence Centre on Information Extraction and Image Understanding for Earth Observation Outline  HR SAR images  Image model: complex GMRF  Bayesian frame  Case study  Evidence maximization information extraction from detected images  Classification Comparison  Conclusion

Competence Centre on Information Extraction and Image Understanding for Earth Observation Block Diagram of Evidence Maximization Algorithm Update θ MAP Estimator EvidenceOptimizer E-step M-step

Competence Centre on Information Extraction and Image Understanding for Earth Observation Evidence Maximization Texture Parameters and Despeckling Amplitude Despeckled image Vertical clique Variance Horizontal clique

Competence Centre on Information Extraction and Image Understanding for Earth Observation Outline  HR SAR images  Image model: complex GMRF  Bayesian frame  Case study  Evidence maximization information extraction from detected images  Classification Comparison  Conclusion

Competence Centre on Information Extraction and Image Understanding for Earth Observation Classification Comparison and Assessment 1/3 Slant rangeGround rangeGround truth Complex GMRF Complex GMRF (*) Evidence Maximization (*) (*) Sampled data of a factor 1/2

Competence Centre on Information Extraction and Image Understanding for Earth Observation Classification Comparison and Assessment 2/3 Slant rangeGround rangeGround truth Complex GMRFComplex GMRF (*)Evidence Maximization (*) (*) Sampled data of a factor 1/2

Competence Centre on Information Extraction and Image Understanding for Earth Observation Classification Comparison and Assessment 3/3 Slant rangeGround rangeGround truth Complex GMRFComplex GMRF (*) Evidence Maximization (*) (*) Sampled data of a factor 1/2

Competence Centre on Information Extraction and Image Understanding for Earth Observation Outline  HR SAR images  Image model: complex GMRF  Bayesian frame  Case study  Evidence maximization information extraction from detected images  Classification Comparison  Conclusion

Competence Centre on Information Extraction and Image Understanding for Earth Observation Conclusion  Complex GMRF is a tool for texture feature extraction.  It is able to model phase correlation pattern.  The comparison with Evidence Maximization algorithm provides that:  Complex GMRF is at about one order of magnitude faster than Evidence Maximization, thanks to the linearity of the model;  Complex GMRF results in a better scene classification: the classes of the image are better represented.

Competence Centre on Information Extraction and Image Understanding for Earth Observation Coming soon…  Further analysis on feature extraction from complex phase;  Statistical analysis of polarimetric data: TerraSAR X dual/quad- polarization product;  Azimuth multi-look analysis;  Study of MDL formalism in relation with ICA;  Conclusion on the best model space;  Integration and validation in KIM. Thank you for your attention!