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


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.


Classification Random forests Integrated learning Remote sensing image Mangroves 



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


  1. 1.
    Alongi DM (2002) Present state and future of the world’s mangrove forests. Environ Conserv 29(3):331–349CrossRefGoogle Scholar
  2. 2.
    Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MathSciNetzbMATHGoogle Scholar
  3. 3.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Carreras X, Marquez L (2001) Boosting trees for anti-spam email filtering. arXiv preprint cs/0109015Google Scholar
  5. 5.
    Chang CC, Lin CJ (2001) LIBSVM: A library for support vector machines. [Online]. Available:
  6. 6.
    Ghimire B, Rogan J, Miller J (2010) Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sens Lett 1(1):45–54CrossRefGoogle Scholar
  7. 7.
    Giri C, Ochieng E, Tieszen L, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011) Status and distribution of mangrove forests of the world using earth observation satellite data. Glob Ecol Biogeogr 20(1):154–159CrossRefGoogle Scholar
  8. 8.
    Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recogn Lett 27(4):294–300CrossRefGoogle Scholar
  9. 9.
    Haralick RM, Shanmugam K, Dinstein IH (1973) “Textural features for image classification,” systems, Man and cybernetics. IEEE Trans 3:610–621Google Scholar
  10. 10.
    Heumann BW (2011) Satellite remote sensing of mangrove forests: recent advances and future opportunities. Prog Phys Geogr 35(1):87–108CrossRefGoogle Scholar
  11. 11.
    Heumann BW (2011) An object-based classification of mangroves using a hybrid decision tree—support vector machine approach[J]. Remote Sens 3(11):2440–2460CrossRefGoogle Scholar
  12. 12.
    Lee S, Kouzania AZ, Hu EJ (2010) Random forest based lung nodule classification aided by clustering. Comput Med Imaging Graph 34(7)Google Scholar
  13. 13.
    Liu K, Li X, Shi X, Wang S (2008) Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands 28(2):336–346CrossRefGoogle Scholar
  14. 14.
    Luo Y, Liao M, Yan J, Zhang C, Shang S (2013) Development and demonstration of an artificial immune algorithm for mangrove mapping using landsat TM. Geosci Remote Sens Lett IEEE 10(4):751–755CrossRefGoogle Scholar
  15. 15.
    McIver D, Friedl M (2002) Using prior probabilities in decision-tree classification of remotely sensed data. Remote sensing of Environment 81(2):253–261CrossRefGoogle Scholar
  16. 16.
    Pal M (2005) Random forest classifier for remote sensing classification[J]. Int J Remote Sens 26(1):217–222CrossRefGoogle Scholar
  17. 17.
    Peddle DR, Franklin SE (1991) Image texture processing and data integration for surface pattern discrimination. Photogramm Eng Remote Sens 57:413–420Google Scholar
  18. 18.
    Rogan J, Franklin J, Roberts DA (2002) A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery. Remote Sens Environ 80(1):143–156CrossRefGoogle Scholar
  19. 19.
    Smith A, Sterba-Boatwright B, Mott J (2010) Novel application of a statistical technique, random forests, bacterial source tracking study. Water Res 44(14)Google Scholar
  20. 20.
    Tadjudin S, Landgrebe DA (1999) Covariance estimation with limited training samples. Geosci Remote Sens IEEE Trans 37(4):2113–2118CrossRefGoogle Scholar
  21. 21.
    Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150CrossRefGoogle Scholar
  22. 22.
    Vapnik V (1995) The nature of statistical learning theory. Springer, New YorkCrossRefzbMATHGoogle Scholar

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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