Graph-Based Method for Multitemporal Segmentation of Sea Ice Floes from Satellite Data

International audience Automated segmentation of the sea ice evolution would allow scientists studying climate change to build accurate models of the sea ice meltdown process, which is a sensitive climate indicator. In this paper, we propose a novel approach which uses shape analysis and graph-based...

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Bibliographic Details
Main Authors: Price, Claudio, Tarabalka, Yuliya, Brucker, Ludovic
Other Authors: Models of spatio-temporal structure for high-resolution image processing (AYIN), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Departamento de Matematica Valparaiso, Universidad Tecnica Federico Santa Maria Valparaiso (UTFSM), NASA Goddard Space Flight Center (GSFC)
Format: Other/Unknown Material
Language:English
Published: HAL CCSD 2013
Subjects:
geo
envir
Sea ice
Online Access:https://hal.inria.fr/hal-00874537/file/price.pdf
https://hal.inria.fr/hal-00874537
Description
Summary:International audience Automated segmentation of the sea ice evolution would allow scientists studying climate change to build accurate models of the sea ice meltdown process, which is a sensitive climate indicator. In this paper, we propose a novel approach which uses shape analysis and graph-based optimization for segmentation of a multiyear ice floe from time series of satellite images. Differently of the state-of-the-art sea ice segmentation techniques, the new method does not rely on the coherence of the intensity values between successive time moments, but only on the coherence of the shape. We successfully validated the performance of the proposed approach on a set of AMSR-E and MODIS images and estimated the area of a sea ice floe as a function of time.

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