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

@article{Li2014ComparisonOC,
  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},
  year={2014},
  volume={6},
  pages={964-983}
}
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
Highly Cited
This paper has 65 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 7 times over the past 90 days. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 45 extracted citations

65 Citations

0102020142015201620172018
Citations per Year
Semantic Scholar estimates that this publication has 65 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 57 references

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

Similar Papers

Loading similar papers…