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Remote Sens. 2014, 6(2), 964-983; doi:10.3390/rs6020964

Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery

1
State Key Laboratory of Remote Sensing Science, and College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
3
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
4
Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720-3114, USA
5
Joint Center for Global Change Studies, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Received: 18 November 2013 / Revised: 10 January 2014 / Accepted: 13 January 2014 / Published: 24 January 2014
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Abstract

Although a large number of new image classification algorithms have been developed, they are rarely tested with the same classification task. In this research, with the same Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, we tested two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms that became popular in remote sensing during the past 20 years. Our analysis focused primarily on the spectral information provided by the TM data. We assessed all algorithms in a per-pixel classification decision experiment and all supervised algorithms in a segment-based experiment. We found that when sufficiently representative training samples were used, most algorithms performed reasonably well. Lack of training samples led to greater classification accuracy discrepancies than classification algorithms themselves. Some algorithms were more tolerable to insufficient (less representative) training samples than others. Many algorithms improved the overall accuracy marginally with per-segment decision making. View Full-Text
Keywords: machine learning; maximum likelihood classification; logistic regression; support vector machine; tree classifiers; random forests machine learning; maximum likelihood classification; logistic regression; support vector machine; tree classifiers; random forests
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Li, C.; Wang, J.; Wang, L.; Hu, L.; Gong, P. Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery. Remote Sens. 2014, 6, 964-983.

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