Mr. Guillaume Oller Profile

Guillaume Oller

at Alcatel Alenia Space France

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Proceedings Article | 12 January 2004 Paper

Correlation and similarity measures for SAR image matching

Guillaume Oller, Loic Rognant, Philippe Marthon

Proceedings Volume 5236, (2004) https://doi.org/10.1117/12.510821

KEYWORDS: Synthetic aperture radar, Binary data, Reflectivity, Statistical analysis, Speckle, Edge detection, Image information entropy, Radiometry, Image analysis, Image resolution

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SAR image matching is a difficult task in relief reconstruction by radargrammetry. Two major class of methods exist : Area based methods and feature based methods. In one hand, feature based methods can give robust, but sparse disparity maps. The difficulty lies in feature extraction. On the other hand, area based methods give dense disparity maps but classical correlation measures are not efficient because of speckle noise. In this paper we deal with various correlation measures evaluation. We propose and compare different ways to estimate the correlation, or the similarity between a couple of SAR image in radargrammetric conditions. Five correlation coefficients will be studied : - the classical Zero Normalized Correlation Coefficient (ZNCC), - a ZNCC applied on a edge image of the scene, - a Binary Correlation Coefficient : we define a binary image of both images and measure the binary overlap. - a correlation coefficient taking into account the Intensity Image statistics, - a correlation coefficient taking into account the Reflectivity Correlation of the underlying scene. We also introduce two similarity measures : - the Cluster Reward Algorithm - and the Mutual Information These kind of operators are based on an entropy measure, and a 2D-histogram analysis to estimate the similarity between the couple of image. They are well adapted to compare images with different radiometry, but similar geometry. In our work, we evaluate the performances of these coefficients with SAR images. We also characterize their behavior on different kind of scenes (textured, high relief area, cities...).

Proceedings Article | 28 January 2002 Paper

SAR images matching improvement in radargrammetric conditions

Guillaume Oller, Leonard Denise, Philippe Marthon, Loic Rognant

Proceedings Volume 4543, (2002) https://doi.org/10.1117/12.453961

KEYWORDS: Synthetic aperture radar, Speckle, Detection and tracking algorithms, Edge detection, 3D modeling, Image processing, Modeling, Image segmentation, Sensors, Objectives

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SAR (Synthetic Aperture Radar) image matching is the most critical step in a radargrammetric chain. The presence of speckle noise makes classical methods (used in optics) inefficient, and some specific techniques must be developed. In this paper, we firstly present the Alcatel's radargrammetric chain : the two images are first resampled in epipolar geometry, to reduce the space search of homologous points, then the matching results are optimized using a TABU search with a regularity constraint. Having the disparity map, we obtain the 3D information by a space triangulation. We also generate a confidence coefficient to determine the most robust disparities. This radargrammetric chain gives encouraging results, but the matching step still raises problems : the area based matching method suffers from speckle, and processing time is considerably increased by the optimization method. So we present secondly the new matching module we are working on, integrating feature based methods. To detect edges we use the ROEWA operator (Ratio Of Exponentially Weighted Average) which is well adapted to SAR images. Edges can be extracted by different ways : watershed algorithm or maximum tracking. The objective is to find the most robust edges in both stereoscopic images, in order to match them and create a first set of matched couples. This will guide our search in the generation of a dense disparity map. We finally propose the global SAR image matching module including edge extraction and matching, radiometric correlation, and eventually user control, to generate an accurate disparity map.

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