Data Models and Information Estimation in Multichannel Radar Remote Sensing Imagery
Description of Tutorial:
Radar, and specially Synthetic Aperture Radars (SAR), has become a well established, active, microwave technique capable of monitoring, and characterizing, the surface of the Earth. In a first period, single channel SAR systems allowed to demonstrate the capacities of this technology to provide Earth surface reflectivity with a high spatial resolution, independently of the weather conditions or the day-night cycle. Nevertheless, the availability of multichannel SAR systems which occurred in the last decade boosted the interest of the remote sensing community in these systems. Multichannel SAR systems open the possibility to increase the information which can be gathered from the scene under observation, since they permit the quantitative estimation of geophysical and biophysical parameters, resulting into a better characterization of the Earth surface. Nowadays, multichannel SAR systems are able to exploit a wide range of diversity sources: single- and multibaseline interferometry (InSAR,) differential interferometry (DinSAR) , polarimetry (PolSAR), polarimetric interferometry (PolInSAR), multitime and multifrequency observations, etc The information estimation process in multichannel SAR imagery is fundamental for a correct exploitation of this information, especially when quantitative estimation is desired due to the presence of different corrupting factors, among which speckle is the most relevant.
The main objective of this tutorial is to provide an in-depth analysis of the information estimation process in multichannel SAR imagery. The complexity and heterogeneity of single and multichannel SAR images makes necessary a statistical characterization of the data. Therefore, the first part of the tutorial will review the statistical models characterizing single and multichannel SAR imagery. This review will address from the simple models aiming to describe homogeneous areas to more complex descriptions trying to extract texture information. The central part of the tutorial addresses the estimation process in single and multichannel SAR data. Firstly, this part will study the different approaches for speckle filtering in single channel SAR images. This analysis will already show that the estimation process in SAR imagery is driven by a series of compromises. In a second stage, multichannel information estimation in SAR imagery, taking into account the most recent theoretical results and estimation techniques published in the literature is presented. The importance of a correct estimation of the complex correlation coefficient in multichannel SAR imagery, with special emphasis in InSAR imagery, will be highlighted. The estimation problem in PolSAR imagery will be considered in detail, paying attention to how an incorrect estimation may damage the estimation of information. The estimation of information in PolInSAR data will be also an objective of this tutorial.
2.- One Dimensional SAR Systems
2.1.- Synthetic Aperture Radar Principles
2.2.- Scatterer Models
2.3.- Wave Scattering Models. Interaction with Matter
2.4.- SAR Data Models and Speckle Noise
3.- Multidimensional SAR Systems
4.- Multidimensional SAR Data Models
5.- Multidimensional SAR Speckle Noise Models
5.1.- Coherence Modeling and Estimation
5.2.- Polarimetric Information Estimation
5.3.- Multidimensional SAR Data Estimation
5.4.- PolInSAR Data Estimation
Material to be Distributed:
The material to be distributed will be composed by all the PowerPoint presentations employed in the tutorial.
About the Speaker
Biography of Presenter:
Carlos López-Martínez received the MSc. degree in electrical engineering and the Ph.D. degree from the Technical University of Catalonia (UPC), Barcelona, Spain, in 1999 and 2003, respectively. In 1999, he joined the Signal Theory and Communications Department, UPC, where he developed his Ph.D. thesis, which focused in multidimensional speckle noise modeling and reduction. From October 2000 until March 2002, he was with the Frequency and Radar Sys- tems Department (HR), German Aerospace Center (DLR), Oberpfa®enhofen, Germany. From June 2003 until December 2005 he has been with the Image and Remote Sensing Group - SAPHIR Team, in the Institute of Electronics and Telecommunications of Rennes (I.E.T.R - CNRS UMR 6164), Rennes, France. In 2006, he was awarded with a Ramón-y-Cajal research contract to continue his research activities at the Remote Sensing Lab. of the Technical University of Catalonia, Barcelona, Spain. His research interests include SAR and multidimensional SAR, radar polarimetry, digital and image signal processing, estimation theory and harmonic analysis. Mr. López-Martínez received the Student Prize Paper Award at the EUSAR 2002 Conference, Cologne, Germany.
L. Pipia, X. Fabregas, A. Aguasca, C. Lopez-Martinez, S. Duque, J.J. Mallorqui, and J. Marturia, "Polarimetric differential sar interferometry : First results with ground-based measurements," Geoscience and Remote Sensing Letters, IEEE, vol. 6, no. 1, pp. 167-171, Jan. 2009.
C. Lopez-Martinez and X. Fabregas, "Model-based polarimetric sar speckle filter," Geoscience and Remote Sensing, IEEE Transactions on, vol. 46, no. 11, pp. 3894-3907, Nov. 2008.
J.S. Lee, T.L. Ainsworth, J. Kelly, and C C. Lopez-Martinez, "Evaluation and bias removal of multi-look effect on entropy/alpha/anisotropy in polarimetric sar decomposition," IEEE Trans. Geosci. Remote Sensing, vol. 46, no. 10, pp. 3039-3052, Oct. 2008.
O. D'hondt, C. López-Martínez, L. Ferro-Famil, and E. Pottier, "Spatially nonstationary anisotropic texture analysis in sar images," IEEE Trans. Geosci. Remote Sensing, vol. 45, no. 12, pp. 3905-3918, Dec 2007.
C. Lopez-Martinez and E. Pottier, "Coherence estimation in synthetic aperture radar data based on speckle noise modeling," Appl. Opt., vol. 46, no. 4, pp. 544 - 558, Feb. 2007.
C. Lopez-Martinez, E. Pottier, and S.R. Cloude, "Statistical assessment of eigenvector-based target decomposition theorems in radar polarimetry," Geoscience and Remote Sensing, IEEE Transactions on, vol. 43, no. 9, pp. 2058-2074, Sept. 2005.
C. Lopez-Martinez and X. Fàbregas, "Polarimetric SAR speckle noise model," IEEE Trans. Geosci. Remote Sensing, vol. 41, no. 10, pp. 2232-2242, Oct. 2003.