On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping

Remote Sensing

Koreen Millard¹  Murray Richardson²  Alisa L. Gallant² 

[1] Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada; E-Mail

关键词: Random Forest;    classification;    training data sample selection;    peatland;    wetland;    LiDAR;   

DOI  :  10.3390/rs70708489

来源: mdpi

【 摘 要 】

Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through a case study in peatland classification using LiDAR derivatives, we present an analysis of the effects of input data characteristics on RF classifications (including RF out-of-bag error, independent classification accuracy and class proportion error). Training data selection and specific input variables (i.e., image channels) have a large impact on the overall accuracy of the image classification. High-dimension datasets should be reduced so that only uncorrelated important variables are used in classifications. Despite the fact that RF is an ensemble approach, independent error assessments should be used to evaluate RF results, and iterative classifications are recommended to assess the stability of predicted classes. Results are also shown to be highly sensitive to the size of the training data set. In addition to being as large as possible, the training data sets used in RF classification should also be (a) randomly distributed or created in a manner that allows for the class proportions of the training data to be representative of actual class proportions in the landscape; and (b) should have minimal spatial autocorrelation to improve classification results and to mitigate inflated estimates of RF out-of-bag classification accuracy.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.