The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 319–326, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-319-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 319–326, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-319-2021

  28 Jun 2021

28 Jun 2021

JOINT LAND COVER AND CROP TYPE MAPPING USING MULTI-TEMPORAL SENTINEL-2 DATA FROM VARIOUS ENVIRONMENTAL ZONES IN GREECE

C. Karakizi, Z. Kandylakis, A. D. Vaiopoulos, and K. Karantzalos C. Karakizi et al.
  • Remote Sensing Laboratory, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zographos, Greece

Keywords: Classification, Time Series, Temporal Features, Datacubes, Stratification

Abstract. In this work, we elaborate on the gained insights from various classification experiments towards detailed land cover mapping over four representative regions of different environmental characteristics in Greece. In particular, the proposed methodology exploits Sentinel-2 data at an annual basis, for the joint classification of 35 land cover and crop type classes. A number of pre-processing steps were employed on the satellite data, in order to address atmospheric and geometric effects, as well as clouds and pertinent shadows. Several classification set-ups were designed and performed using either time series of spectral features or temporal features. The latter consisted of statistical metrics, derived from the spectral time series, and therefore were significantly reduced in dimension. Experiments using the Random Forest algorithm were performed by building several per-tile models, as well as cross- regional models based on training data from all considered regions/tiles. Overall classification accuracy rates exceeded 90% for most experiments. Further analysis on the experimental results highlighted that crop types were classified more accurately when using the spectral time series features, compared to the temporal ones. Classification accuracy for non-crop classes proved much less affected by the type of employed features. The inclusion of auxiliary data layers was beneficial in all cases, both for overall and for per-class accuracy metrics. Qualitative evaluation on the predicted maps further affirmed the efficiency of the developed methodology.