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.
Keywords: machine learning; maximum likelihood classification; logistic regression; support vector machine; tree classifiers; random forests
This is an open access article distributed under the
Creative Commons Attribution License which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is properly cited.
Export to BibTeX
MDPI and ACS Style
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.
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 Sensing. 2014; 6(2):964-983.
Li, Congcong; Wang, Jie; Wang, Lei; Hu, Luanyun; Gong, Peng. 2014. "Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery." Remote Sens. 6, no. 2: 964-983.