ANR PHOENIX

Workgroup GMoveVision3D

[Glacier Move Vision 3D]

Context : Glacier displacement analysis from stereoscopic time lapses

Automatic digital cameras installed on the ground (ground based cameras) are increasingly used to observe natural phenomena (glacier, rivers, landslides, etc.). The simultaneous use of two stereo configuration devices delivers datasets that can be processed by using two approaches:

· 2x2D+T Displacement Analysis followed by a 3D+T Displacement-Image Synthesis: two time series of images (time lapses), right and left hand sides, can be processed separately for change detection and computation of 2D displacement. Depth information is needed to combine these displacement fields and derive further a 3D displacement field.

· 3D+T Image Synthesis and 3D+T Displacement Analysis: stereo pairs allow to obtain 3D images by photogrammetry concerns. The time series of 3D images can then be processed in order to compute, directly, the 3D displacement field.

Which is the best, accurate, strategy among the two approaches?

Notice that the observed scene is a natural and wide range environment (Alpine glaciers)! This makes each of these two approaches intricate… Indeed, due to highly variable climatic and temperature conditions, internal and external parameters of cameras (structure expansion and deformation, slight movement and vibrations) are permanently changing. The consequence is a lack of stable camera calibration. In addition, the scene is subject to variable illumination… all these induce a set of disturbances that lead to significant measurement uncertainty and make it difficult to observe small object displacements (rock, ice block, serac fall).

The main goal of GMoveVision3D is to propose accurate glacier texture descriptors for stereo matching and 3D+T displacement monitoring over French Alp glaciers.

Team

· Emmanuel TROUVÉ (principal investigator)

· Flavien VERNIER (investigator)

· Abdourrahmane M. ATTO (investigator)

· Héla HADHRI (investigator, PhD student)

· Alexandre BENOIT (expert)

· Florence TUPIN (expert)

· Jean-Marie NICOLAS (expert)

Literature

[ZF2003] B. Zitova, J. Flusser, Image registration methods: a survey, Elsevier Image and Vision Computing No. 21, pp. 977–1000, 2003.

[HZ2004] R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, Second Edition, Cambridge University Press, March 2004.

[LMMMPS2004] R. Lanari, O. Mora, M. Manunta, J. Mallorqui, P. Berardino, and E. Sansosti, A Small-Baseline Approach for Investigating Deformations on Full-Resolution Differential SAR Interferograms, IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 7, pp. 1377–1386, 2004.

[CMPL2011] F. Casu, A. Manconi, A. Pepe, and R. Lanari, Deformation Time-Series Generation in Areas Characterized by Large Displacement Dynamics: The SAR Amplitude Pixel-Offset SBAS Technique, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 99, pp. 1–12, 2011.

[TYGP2013] Z. Tang and Y. Yang and X. Guo and B. Prabhakaran, Distributed Haptic Interactions with Physically-Based 3D Deformable Models over Lossy Networks, IEEE Transactions on Haptics, Vol. 6, No. 4, pp. 417-428, 2013.

[APM2010] A. M. Atto and D. Pastor and G. Mercier, Wavelet Packets of fractional Brownian motion: Asymptotic Analysis and Spectrum Estimation , IEEE Transactions on Information Theory, Vol. 56, No. 9, 2010.

[ABB2013] A. M. Atto and Y. Berthoumieu and P. Bolon, 2-Dimensional Wavelet Packet Spectrum for Texture Analysis, Accepted for publication, IEEE Transactions on Image Processing, v. 22, no 6, 2013.

[Pham2015] Ha-Tai Pham, Analyse de « Time Lapse » optiques stéréo et d’images radar satellitaires : application à la mesure du déplacement des glaciers, thèse de doctorat de l’Université Grenoble Alpes, février 2015.