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Evaluation of Multiple Classifier Systems for Landslide Identification in LANDSAT Thematic Mapper (TM) Images

Transportation Engineering Department of Polytechnic College, University of Sao Paulo, São Paulo 05507-070, Brazil
Cartography Department of Geosciences, Institute of Federal, University of Minas Gerais, Belo Horizonte 31270-901, Brazil
Department of Transportation Engineering, Polytechnic School of the University of Sao Paulo, São Paulo 05507-070, Brazil
Author to whom correspondence should be addressed.
Academic Editors: Jason K. Levy and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(9), 164;
Received: 24 May 2016 / Revised: 29 July 2016 / Accepted: 1 September 2016 / Published: 13 September 2016
PDF [4544 KB, uploaded 13 September 2016]


Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for landslide risk management, remote sensing usage does have a few drawbacks. The aforementioned events commonly occur at high steep slope regions, frequently associated with shadow occurrence in satellite images, which impairs the identification process and results in low accuracy classifications. In this sense, this paper aims to evaluate the accuracy of different ensembles of multiple classifier systems (MCSs) for landslide scar identification. A severe landslide event on a steep slope with a high rainfall rate area in the southeast region of Brazil was chosen. Ten supervised classifiers were used to identify this severe event and other possible features for the LANDSAT thematic mapper (TM) from June of 2000. The results were evaluated, and nine MCSs were constructed based on the accuracy of the classifiers. Voting was applied through the ensemble method, coupled with contextual analysis and random selection tie-breaker methods. Accuracy was evaluated for each classification ensemble, and a progressive enhancement in the ensemble accuracy was noted as the least accurate classifiers were removed. The best accuracy for landslide identification emerged from the ensemble of the three most accurate classification results. In summary, MCS application generally improved the classification quality and led to fewer omission errors, coupled with a better classification percentage for the ‘landslide’ class. However, the MCS ensemble algorithm selection must be customized to the purpose of the classification. It is crucial to assess single accuracy indicators of each algorithm to ascertain those with the most consistent performance regarding the final results. View Full-Text
Keywords: multiple classifier system; landslide scars; accuracy evaluation multiple classifier system; landslide scars; accuracy evaluation

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Manfré, L.A.; De Albuquerque Nóbrega, R.A.; Quintanilha, J.A. Evaluation of Multiple Classifier Systems for Landslide Identification in LANDSAT Thematic Mapper (TM) Images. ISPRS Int. J. Geo-Inf. 2016, 5, 164.

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