ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 813–820, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-813-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 813–820, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-813-2020

  03 Aug 2020

03 Aug 2020

MULTI-MODAL DEEP LEARNING WITH SENTINEL-3 OBSERVATIONS FOR THE DETECTION OF OCEANIC INTERNAL WAVES

L. Drees1, J. Kusche1, and R. Roscher1,2 L. Drees et al.
  • 1IGG, University of Bonn, Germany
  • 2Institute of Computer Science, University of Osnabrueck, Germany

Keywords: multi-modal, deep learning, internal waves, sentinel-3, multi-stream, late fusion, neural network

Abstract. The observation of waves that propagate along density interfaces inside the ocean poses a significant challenge, as their visible surface signatures are much lower compared to their internal amplitudes. However, monitoring internal waves is important as they redistribute large amounts of energy, play a role in mixing and vertical heat transfer, and modify water and nutrient transports. Although satellite observations would allow global monitoring of internal waves at constant time intervals, their automatic detection is challenging: In optical images, internal waves are hardly visible and can be obscured by clouds, whereas radar data have limitations in coastal regions and their spatial coverage is not perfect. Furthermore, the occurrence of internal waves can be confused with other ocean phenomena. In this work, we present an automated detection framework for internal waves based on multiple data sources in order to compensate for the shortcoming of single data sources. In our application, we use Ocean and Land Color Imager and Synthetic Aperture Radar Altimeter data. Our contributions are (1) we develop a multi-modal deep neural network SONet with multi-streams and late fusion, which performs a classification on the basis of training with both modalities, and (2) we establish a method to deal with missing modalities. Experiments in the Amazon Shelf region show SONet achieves adequate results when both modalities are available, but also when only a single modality is available. By exploiting correlations between the modalities, SONet classifies OLCI images off the SRAL ground track better than uni-modal network ONet, which describes a great advantage of our multi-modal network.