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

Geodesy and Cartography

Drzewiecki Wojciech¹ 

[1] AGH University, Faculty of Mining Surveying and Environmental Engineering, Department of Geoinformation, Photogrammetry and Remote Sensing of Environment, al. Mickiewicza 30, 30-059 Kraków, Poland;

关键词: machine learning;    model ensembles;    sub-pixel classification;    impervious areas;    Landsat;   

DOI  :  10.1515/geocart-2016-0016

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown