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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alongi DM (2002) Present state and future of the world’s mangrove forests. Environ Conserv 29(3):331–349
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Carreras X, Marquez L (2001) Boosting trees for anti-spam email filtering. arXiv preprint cs/0109015
Chang CC, Lin CJ (2001) LIBSVM: A library for support vector machines. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm
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):45–54
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):154–159
Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recogn Lett 27(4):294–300
Haralick RM, Shanmugam K, Dinstein IH (1973) “Textural features for image classification,” systems, Man and cybernetics. IEEE Trans 3:610–621
Heumann BW (2011) Satellite remote sensing of mangrove forests: recent advances and future opportunities. Prog Phys Geogr 35(1):87–108
Heumann BW (2011) An object-based classification of mangroves using a hybrid decision tree—support vector machine approach[J]. Remote Sens 3(11):2440–2460
Lee S, Kouzania AZ, Hu EJ (2010) Random forest based lung nodule classification aided by clustering. Comput Med Imaging Graph 34(7)
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):336–346
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):751–755
McIver D, Friedl M (2002) Using prior probabilities in decision-tree classification of remotely sensed data. Remote sensing of Environment 81(2):253–261
Pal M (2005) Random forest classifier for remote sensing classification[J]. Int J Remote Sens 26(1):217–222
Peddle DR, Franklin SE (1991) Image texture processing and data integration for surface pattern discrimination. Photogramm Eng Remote Sens 57:413–420
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):143–156
Smith A, Sterba-Boatwright B, Mott J (2010) Novel application of a statistical technique, random forests, bacterial source tracking study. Water Res 44(14)
Tadjudin S, Landgrebe DA (1999) Covariance estimation with limited training samples. Geosci Remote Sens IEEE Trans 37(4):2113–2118
Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Luo, YM., Huang, DT., Liu, PZ. et al. An novel random forests and its application to the classification of mangroves remote sensing image. Multimed Tools Appl 75, 9707–9722 (2016). https://doi.org/10.1007/s11042-015-2906-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-015-2906-9