Random Forest (RF) Wrappers for Waveband Selection and Classification of Hyperspectral Data

N.K. Poona, Adriaan Van Niekerk, R.L. Nadel, R. Ismail

    Research output: Contribution to journalArticle

    16 Citations (Scopus)


    © Society for Applied Spectroscopy. © The Author(s) 2015. Hyperspectral data collected using a field spectroradiometer was used to model asymptomatic stress in Pinus radiata and Pinus patula seedlings infected with the pathogen Fusarium circinatum. Spectral data were analyzed using the random forest algorithm. To improve the classification accuracy of the model, subsets of wavebands were selected using three feature selection algorithms: (1) Boruta; (2) recursive feature elimination (RFE); and (3) area under the receiver operating characteristic curve of the random forest (AUC-RF). Results highlighted the robustness of the above feature selection methods when used in conjunction with the random forest algorithm for analyzing hyperspectral data. Overall, the Boruta feature selection algorithm provided the best results. When discriminating F. circinatum stress in Pinus radiata seedlings, Boruta selected wavebands (n = 69) yielded the best overall classification accuracies (training error of 17.00%, independent test error of 17.00% and an AUC value of 0.91). Classification results were, however, significantly lower for P. patula seedlings, with a training error of 24.00%, independent test error of 38.00%, and an AUC value of 0.65. A hybrid selection method that utilizes combinations of wavebands selected from the three feature selection algorithms was also tested. The hybrid method showed an improvement in classification accuracies for P. patula, and no improvement for P. radiata. The results of this study provide impetus towards implementing a hyperspectral framework for detecting stress within nursery environments.
    Original languageEnglish
    Pages (from-to)322-333
    JournalApplied Spectroscopy
    Issue number2
    Publication statusPublished - 2016

    Fingerprint Dive into the research topics of 'Random Forest (RF) Wrappers for Waveband Selection and Classification of Hyperspectral Data'. Together they form a unique fingerprint.

    Cite this