Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Application of Random Forests to Assessment of Importance of Variables in Multi-sensor Data Fusion for Land-cover Classification

  • Park No-Wook (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Chi kwang-Hoon (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources)
  • Published : 2006.06.01
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Abstract

A random forests classifier is applied to multi-sensor data fusion for supervised land-cover classification in order to account for the importance of variable. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. The distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Two different multi-sensor data sets for supervised classification were used to illustrate the applicability of random forests: one with optical and polarimetric SAR data and the other with multi-temporal Radarsat-l and ENVISAT ASAR data sets. From the experimental results, the random forests approach could extract important variables or bands for land-cover discrimination and showed reasonably good performance in terms of classification accuracy.

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References

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