Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier

Tianxiang Zhang, Jinya Su, Zhiyong Xu, Yulin Luo, Jiangyun Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)
3 Downloads (Pure)


Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water, and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision, and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable.

Original languageEnglish
Article number543
Number of pages17
JournalApplied Sciences (Switzerland)
Issue number2
Publication statusPublished - 8 Jan 2021

Bibliographical note

Funding: This work was supported by the Fundamental Research Funds for the China Central Universities of USTB (FRF-DF-19-002), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (BK20BE014).

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is
not applicable to this article.


  • Bayesian optimization
  • Hyperparameter tuning
  • Land cover classification
  • Random forest
  • Sentinel-2 satellite
  • Urban management


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