Improving random forest with ensemble of features and semi-supervised feature extraction

Junshi Xia, Wenzhi Liao, Jocelyn Chanussot, Peijun Du, Guanghan Song, Wilfried Philips, Alejandro Frery

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


In this letter, we propose a novel approach for improving Random Forest (RF) in hyperspectral image classification. The proposed approach combines the ensemble of features and the semisupervised feature extraction (SSFE) technique. The main contribution of our approach is to construct an ensemble of RF classifiers. In this way, the feature space is divided into several disjoint feature subspaces. Then, the feature subspaces induced by the SSFE technique are used as the input space to an RF classifier. This method is compared with a regular RF and an RF with the reduced features by the SSFE on two real hyperspectral data sets, showing an improved performance in ill-posed, poor-posed, and well-posed conditions. An additional study shows that the proposed method is less sensitive to the parameters.
Original languageEnglish
Pages (from-to)1471-1475
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number7
Publication statusPublished - 16 Mar 2015


  • ensemble learning
  • semi-supervised feature extraction
  • classification
  • hyperspectral image
  • random forest
  • image classification
  • hyperspectral data
  • selection
  • accuracy
  • geophysical image processing


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