The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1343–1346, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1343-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1343–1346, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1343-2020

  14 Aug 2020

14 Aug 2020

FLOOD DETECTION IN TIME SERIES OF OPTICAL AND SAR IMAGES

C. Rambour1,2, N. Audebert1, E. Koeniguer2, B. Le Saux2, M. Crucianu1, and M. Datcu1 C. Rambour et al.
  • 1CEDRIC (EA4629), Conservatoire National des Arts et Métiers, HESAM Université, 75003 Paris, France
  • 2ONERA / DTIS, Université Paris-Saclay, F-91123 Palaiseau, France

Keywords: co-registered optical and SAR images, time series, flood event detection, SEN12-FLOOD dataset, machine learning, neural network approach

Abstract. These last decades, Earth Observation brought a number of new perspectives from geosciences to human activity monitoring. As more data became available, Artificial Intelligence (AI) techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover. Yet, machine learning on SAR data is still considered challenging due to the lack of available labeled data. To help the community go forward, we introduce a new dataset composed of co-registered optical and SAR images time series for the detection of flood events and new neural network approaches to leverage these two modalities.