An novel random forests and its application to the classification of mangroves remote sensing image
- 456 Downloads
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.
KeywordsClassification 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).
- 4.Carreras X, Marquez L (2001) Boosting trees for anti-spam email filtering. arXiv preprint cs/0109015Google Scholar
- 5.Chang CC, Lin CJ (2001) LIBSVM: A library for support vector machines. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 9.Haralick RM, Shanmugam K, Dinstein IH (1973) “Textural features for image classification,” systems, Man and cybernetics. IEEE Trans 3:610–621Google Scholar
- 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
- 17.Peddle DR, Franklin SE (1991) Image texture processing and data integration for surface pattern discrimination. Photogramm Eng Remote Sens 57:413–420Google Scholar
- 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