Hierarchical classification of Sentinel 2-a images for land use and land cover mapping and its use for the CORINE system

Demirkan D. C. , Koz A., Duzguna H. S.

JOURNAL OF APPLIED REMOTE SENSING, vol.14, no.2, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.1117/1.jrs.14.026524
  • Keywords: Sentinel-2, land use land cover, coordination of information on the environment, hierarchical classification, support vector machine, textural feature extraction


The aim of this study is to investigate the potential of the Sentinel-2 satellite for land use and land cover (LULC) mapping. The commonly known supervised classification algorithms, support vector machines (SVMs), random forest (RF), and maximum likelihood (ML) classification are adopted for investigation along with a proposed hierarchical classification model based on a coordination of information on the environment land cover system. The main classes for land cover and mapping in the proposed hierarchical classification are selected as water, vegetation, built-up, and bare land in the first level, which is followed by inland water, marine water, forest/meadow, vegetated agricultural land, barren land, and nonvegetated agricultural land in the second level. The study areas for the experiments are selected as the two biggest cities of Turkey, namely Ankara and Izmir, providing a sufficient number of classes for comparison purposes. During the utilized hierarchical methodology, water and vegetation are first extracted using the normalized difference water and vegetation indices. This is followed by the selection of training pixels from the remaining classes to perform and compare different supervised learning algorithms for the first- and second-level classification in terms of accuracy rates. The experimental results first reveal that while the SVMs have close accuracy performances to those with RF, they are significantly superior to the ML classification, with an average of 8% accuracy rates for LULC mapping. Second, the hierarchical classification also gives higher performances with respect to the nonhierarchical classification, with the provided gains between 4% and 10% for class-based accuracies. The overall accuracy rates of the proposed hierarchical methodology are 85% and 84% for the first-level classes and 83% and 72% for the second-level classes, respectively, for Izmir and Ankara. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)