Image and Signal Processing Methods

Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

[+] Author Affiliations
Zaakirah Bassa, Urmilla Bob, Riyad Ismail

University of KwaZulu-Natal, School of Environmental Sciences, King George V Avenue, Durban 4041, South Africa

Zoltan Szantoi

Land Resource Management Unit, Joint Research Centre, European Commission, Via Enrico Fermi 2749, Ispra 21027, Italy

J. Appl. Remote Sens. 10(1), 015017 (Mar 11, 2016). doi:10.1117/1.JRS.10.015017
History: Received September 15, 2015; Accepted February 16, 2016
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Abstract.  In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the “one-against-one” and “one-against-all” combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (>80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) >80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Zaakirah Bassa ; Urmilla Bob ; Zoltan Szantoi and Riyad Ismail
"Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms", J. Appl. Remote Sens. 10(1), 015017 (Mar 11, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.015017


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