Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

• Main Author: Drzewiecki Wojciech
• Format: Article
• Language: English
• Published: Polish Academy of Sciences 2016-12-01
• Series: Geodesy and Cartography
• Subjects: machine learning; model ensembles; sub-pixel classification; impervious areas; Landsat
• Online Access: http://www.degruyter.com/view/j/geocart.2016.65.issue-2/geocart-2016-0016/geocart-2016-0016.xml?format=INT

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.