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Multimedia Tools and Applications

, Volume 75, Issue 16, pp 9707–9722 | Cite as

An novel random forests and its application to the classification of mangroves remote sensing image

  • Yan-Min Luo
  • De-Tian Huang
  • Pei-Zhong Liu
  • Hsuan-Ming FengEmail author
Article

Abstract

The novel random forests algorithm with variables random input and random combination (Forest_RI_RC) machine was proposed to improve the weakness of low accuracy and over-fitting phenomenon in single decision tree. The proposed method produces more and more selections and combinations to increase the possibility of the best decision-making features. This way reduces the correlation coefficient of the random forests, which efficiently lead to the lower generalization error and approach the higher classification accuracy. The standard machine learning datasets were used to verify the validity of the classification. The simulation results showed that the novel algorithm with the multiple classifiers to concurrently segment the objects and achieve the smaller generalization error. Finally, the algorithm was applied to the classified problems of mangrove remote sensing image. Software simulations presents that the classification accuracy is basically stable at around 90 %. This performance is better than the other two decision tree and bagging methods.

Keywords

Classification Random forests Integrated learning Remote sensing image Mangroves 

Notes

Acknowledgments

This work was supported by the Talent project of Huaqiao University (No. 14BS215) and Quanzhou scientific and technological planning projects of Fujian, China (2015Z120).

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Computer Science & TechnologyHuaqiao UniversityXiamenChina
  2. 2.College of EngineeringHuaqiao UniversityQuanzhouChina
  3. 3.Department of Computer Science and Information EngineeringNational Quemoy UniversityKinmenTaiwan

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