Abstract
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.
The results proved that in case of sub-pixel evaluation the most accurate prediction of
change may not necessarily be based on the most accurate individual assessments. When
single methods are considered, based on obtained results Cubist algorithm may be advised
for Landsat based mapping of imperviousness for single dates. However, Random Forest
may be endorsed when the most reliable evaluation of imperviousness change is the
primary goal. It gave lower accuracies for individual assessments, but better prediction
of change due to more correlated errors of individual predictions.
Heterogeneous model ensembles performed for individual time points assessments at
least as well as the best individual models. In case of imperviousness change assessment
the ensembles always outperformed single model approaches. It means that it is possible
to improve the accuracy of sub-pixel imperviousness change assessment using ensembles
of heterogeneous non-linear regression models.
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