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

  title={Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery},
  author={Congcong Li and Jie Wang and Lei Wang and Luanyun Hu and Peng Gong},
  journal={Remote Sensing},
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… CONTINUE READING
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Publications referenced by this paper.
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Comparison of different gray-level reduction schemes for a revised texture spectrum method for land-use classification using IKONOS imagery

  • B. Xu, P. Gong, E. Seto, R. Spear
  • Photogramm. Eng. Remote Sens. 2003,
  • 2014

A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images

  • S. S. Cai, D. S. Liu
  • Remote Sens. Lett. 2013,
  • 2013

Clustering based on eigenspace transformation—CBEST for efficient classification

  • Y. Chen, P. Gong
  • ISPRS J. Photogramm. Remote Sens. 2013,
  • 2013
1 Excerpt

Finer resolution observation and monitoring of global land cover: First mapping results with landsat TM and ETM+ data

  • P. Gong, J. Wang, +6 authors S Liu
  • Int. J. Remote Sens. 2013,
  • 2013
1 Excerpt

Object features for pixel-based classification of urban areas comparing different machine learning algorithms

  • N. Wolf
  • Photogramm. Fernerkund. 2013,
  • 2013

Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends

  • Q. H. Weng
  • Remote Sens. Environ
  • 2012
1 Excerpt

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