Forest resource classification based on random forest and object oriented method

doi:10.11947/j.AGCS.2020.20190272

Abstract

Abstract: Given that there are few studies on forest resource classification with the lack of relatively simple and effective methods, a forest resource classification method integrating object-oriented segmentation and random forest is proposed in this paper. Object-oriented segmentation technology could efficiently reduce the "salt and pepper effect", and random forest classification algorithm has the advantages of high accuracy, strong anti-noise ability and satisfying stability. Therefore, we built the optimum random forest classification model by adjusting the object-oriented segmentation parameters, constructing the optimal feature space and estimating the number of decision trees in random forests. Besides, the SVM algorithm is taken into comparison. The results show that the overall accuracy of the classification algorithm in this study is 83.34%with the Kappa coefficient reaching 0.789 2, which are significantly higher than that of SVM algorithm. It proves that object-oriented random forest classification can effectively improve the accuracy of forest resource classification.

Key words: forest resource classification, object-oriented method, random forest

CLC Number:

P237

WANG Meng, ZHANG Xinchang, WANG Jiayao, SUN Ying, JIAN Ge, PAN Cuihong. Forest resource classification based on random forest and object oriented method[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(2): 235-244.

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