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Impervious surfaces coverage estimation was done using decision and regression trees based on C5.0 and Cubist algorithms. At the stage of classification the research area was divided into two categories: i) completely permeable (imperviousness index less than 1%) and ii) fully or partially impervious areas. At the stage of sub-pixel classification evaluation of percentage impervious surfaces coverage within single pixel was done. Based on the results of cross-validation, we selected the approaches guaranteeing the lowest means errors in terms of training set. Accuracy of the imperviousness index estimation was checked based on validation data set. The average error of hard classification using spectral features only was 6.5% and about 4.4% for spectral features combining with absolute gradient-based characteristics. The root mean square error (RMSE) of determination of the percentage impervious surfaces coverage within a single pixel was equal to 9.46% for the best tested classification features sets. The two-stage procedure was utilized for the primary approach involving spectral bands as the classification features set and for the approach guaranteeing the best accuracy for classification and regression stage. The results have shown that inclusion of textural measures into classification features can improve the estimation of imperviousness based on Landsat imagery. However, it seems that in our study this is mainly due higher accuracy of hard classification used for masking out the completely permeable pixels. |
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CITATIONS
Cited by 2 scholarly publications.
Earth observing sensors
Landsat
Data modeling
Error analysis
Image classification
Binary data
Analytical research
