Abstract

MANCERA-FLOREZ, Juan Ricardo  and  LIZARAZO, Ivan. Land cover classification at three different levels of detail from optical and radar Sentinel SAR data: a case study in Cundinamarca (Colombia). Dyna rev.fac.nac.minas [online]. 2020, vol.87, n.215, pp.136-145.  Epub Jan 12, 2021. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v87n215.84915.

In this paper, the potential of Sentinel-1A and Sentinel-2A satellite images for land cover mapping is evaluated at three levels of spatial detail; exploratory, reconnaissance, and semi-detailed. To do so, two different image classification approaches are compared: (i) a traditional pixel-wise approach; and (ii) an object-oriented approach. In both cases, the classification task was conducted using the “RandomForest” algorithm. The case study was also intended to identify a set of radar channels, optical bands, and indices that are relevant for classification. The thematic accuracy of the classifications displays the best results for the object-oriented approach to exploratory and recognition levels. The results show that the integration of multispectral and radar data as explanatory variables for classification provides better results than the use of a single data source.

Keywords : Sentinel-1A; Sentinel-2A; land cover classification; random forest; object-based analysis.

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