The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W16
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W16, 83–89, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W16-83-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W16, 83–89, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W16-83-2019

  17 Sep 2019

17 Sep 2019

MACHINE LEARNING FOR SEA ICE MONITORING FROM SATELLITES

C. O. Dumitru, V. Andrei, G. Schwarz, and M. Datcu C. O. Dumitru et al.
  • German Aerospace Center (DLR), Remote Sensing Technology Institute, Münchener Str. 20, 82234, Weßling, Germany

Keywords: Copernicus, change detection, machine learning, sea ice, maps, data analytics

Abstract. Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. To this end, we applied modern machine learning techniques to produce ice-describing semantic maps of the polar regions of the Earth. Time series of these maps can then be exploited for local and regional change maps of selected areas. What we expect, however, are fully-automated unsupervised routine classifications of sea ice regions that are needed for the rapid and reliable monitoring of shipping routes, drifting and disintegrating icebergs, snowfall and melting on ice, and other dynamic climate change indicators. Therefore, we designed and implemented an automated processing chain that analyses and interprets the specific ice-related content of high-resolution synthetic aperture radar (SAR) images. We trained this system with selected images covering various use cases allowing us to interpret these images with modern machine learning approaches. In the following, we describe a system comprising representation learning, variational inference, and auto-encoders. Test runs have already demonstrated its usefulness and stability that can pave the way towards future artificial intelligence systems extending, for instance, the current capabilities of traditional image analysis by including content-related image understanding.