Cluster Computing

, Volume 20, Issue 3, pp 2311–2321 | Cite as

Spectral-spatial multi-feature classification of remote sensing big data based on a random forest classifier for land cover mapping

  • X. M. Zhang
  • G. J. HeEmail author
  • Z. M. Zhang
  • Y. Peng
  • T. F. Long


Supplementary information, such as multi-temporal spectral data and textural features, has the potential to improve land cover classification accuracy. However, given the larger volumes of remote sensing data, it is difficult to utilize all the features of remote sensing big data having different times and spatial resolutions. Inefficiency is also a large problem when dealing with large area land cover mapping. In this study, a new mode of incorporating spatial and temporal dependencies in a complex region employing the random forests (RFs) classifier was utilized. To map land covers, spring and autumn spectral images and their spectral indexes, textural features obtained from Landsat 5 were selected, and an importance measure variable was used to reduce the data’s dimension. In addition to randomly selecting the variable, we used random sampling to furthest decrease the generalization error in RF. The results showed that utilizing random sampling, multi-temporal spectral image and texture features, the classification of the Wuhan urban agglomeration, China, using RF performed well. The RF algorithm yielded an overall accuracy of 89.2% and a Kappa statistic of 0.8522, indicating high model performance. In addition, the variable importance measures demonstrated that the type of textural features was extremely important for intra-class separability. The RF model has transitivity. The algorithm can be extended by choosing a set of appropriate features for signature extension over large areas or in time-series of Landsat imagery. Land cover mapping might be more economical and efficient if no-cost imagery is used.


Land cover mapping Random forests (RFs) Random sampling Spectral features Temporal features Textural features Feature selection (FS) Wuhan urban agglomeration 



This research has been supported by the National Research Program on Global Changes and Adaptation: Rapid production method of large scale global change products (2016YFA0600302) and by National Ecological Environment Change Assessment by Remote Sensing Survey Project 2000–2010 (STSN-10-03) Grants.


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© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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