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, 557–564, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-557-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 557–564, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-557-2020

  03 Aug 2020

03 Aug 2020

CAN SPOT-6/7 CNN SEMANTIC SEGMENTATION IMPROVE SENTINEL-2 BASED LAND COVER PRODUCTS? SENSOR ASSESSMENT AND FUSION

O. Stocker and A. Le Bris O. Stocker and A. Le Bris
  • LASTIG, Université Gustave Eiffel, ENSG, IGN, F-94160 Saint-Mandé, France

Keywords: land cover, semantic segmentation, satellite, deep learning, CNN, SPOT-6/7, Sentinel-2

Abstract. Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels.