Journal
REMOTE SENSING OF ENVIRONMENT
Volume 170, Issue -, Pages 115-120Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2015.09.008
Keywords
C5.0; Classification tree analysis; Classification algorithms; Logistic model trees; Multivariate adaptive regression splines; Random forest; Support vector machines
Funding
- United States Geological Survey [08HQGR0157]
Ask authors/readers for more resources
Evaluation of classification methods, whether in connection with the development of new methods or in an application setting, has been hampered by the lack of availability of adequate data and an approach for comparisons. We collected 30 mostly moderate-resolution, multispectral datasets to enable statistically rigorous comparisons of methods and have made those datasets available for other researchers. We developed a methodological approach to comparing classification methods and demonstrated the approach using six methods, C5.0, classification tree analysis, logistic model trees, multivariate adaptive regression splines, random forest, and support vector machines. We also demonstrated how these data and this approach can be used to address specific questions in addition to overall accuracy performance, including the relative effects of using derived components and ancillary data and the relative success in classifying rare classes. Most methods performed best by at least one metric with at least one dataset Therefore, although random forest on average performed statistically significantly better than the other methods tested, we do not recommend this method as the sole option currently in remote sensing. Rather, our results suggest that remote sensing analysts should evaluate multiple methods with respect to any classification project, which can be accomplished through statistical software packages. (C) 2015 Elsevier Inc All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available