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Evaluation of rule-based classifier for Landsat-based automated land cover mapping in South Africa


Salmon, BP and Wessels, KJ and van den Bergh, F and Steenkamp, KC and Kleynhans, W and Swanepoel, D and Roy, DP and Kovalskyy, V, Evaluation of rule-based classifier for Landsat-based automated land cover mapping in South Africa, Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 21-26 July 2013, Melbourne, Australia, pp. 4301-4304. ISBN 978-1-4799-1114-1 (2013) [Refereed Conference Paper]

Copyright Statement

Copyright 2013 IEEE

DOI: doi:10.1109/IGARSS.2013.6723785


This study investigated the automated pre-processing and land cover classification of Landsat data. The Web-enabled Landsat Data (WELD) system was used to process large volumes of Landsat imagery to calibrated top of atmosphere reflectance and brightness temperature products which are composited temporally and mosaicked for the KwaZulu-Natal Province of South Africa. The usefulness of an Automatic Spectral Rule-base Classifier (ASRC) approach was evaluated by relating the produced spectral categories to land cover classes. The ASRC method uses a hierarchical rule set, which relies on universally set thresholds derived from the literature, to decide on the spectral category. To assess the performance, the spectral categories were treated as input features to supervised classifiers to optimally assign land cover labels. The land cover classes used in the experiments were obtained from the official map of the Kwazulu-Natal province in South Africa, which was generated by operators in 2008. This approach was compared to an experiment using the original 7 Landsat spectral bands and derived indices as input features. It was found that the ASRC spectral categories did not provide a useful translation to land cover classes (45.5% classification accuracy), while the experiments using the Landsat 7 spectral bands or indices did considerably better (82.7% classification accuracy).

Item Details

Item Type:Refereed Conference Paper
Keywords:change detection, classification, random forest, satellite, time series, classification algorithms, knowledge based systems, pattern recognition, remote sensing
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Salmon, BP (Dr Brian Salmon)
ID Code:87153
Year Published:2013
Web of Science® Times Cited:2
Deposited By:Engineering
Deposited On:2013-11-08
Last Modified:2014-08-05

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