Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6348548 | International Journal of Applied Earth Observation and Geoinformation | 2016 | 15 Pages |
Abstract
Random Forest (RF), which is an ensemble decision tree learning technique, was used in this study. RF performs parameter subset selection as a part of its classification procedure. In this study, parameter subsets were obtained and analyzed to infer scattering mechanisms useful for urban area classification. The Cloude-Pottier α, the Touzi dominant scattering amplitude αs1 and the anisotropy A were among the top six important parameters selected for both the datasets. However, it was observed that these parameters were ranked differently for the two datasets. The urban area classification using RF was compared with the Support Vector Machine (SVM) and the Maximum Likelihood Classifier (MLC) for both the datasets. RF outperforms SVM by 4% and MLC by 12% in Dataset 1. It also outperforms SVM and MLC by 3.5% and 11% respectively in Dataset 2.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Siddharth Hariharan, Siddhesh Tirodkar, Avik Bhattacharya,