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Remote Sens. 2017, 9(11), 1205; https://doi.org/10.3390/rs9111205

An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification

1
College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Fabian Löw, Qi Wang, Farid Melgani and Prasad S. Thenkabail
Received: 21 September 2017 / Revised: 19 November 2017 / Accepted: 21 November 2017 / Published: 22 November 2017
View Full-Text   |   Download PDF [4242 KB, uploaded 22 November 2017]   |  

Abstract

Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA) and a boosting naïve Bayesian tree (NBTree), is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting naïve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting naïve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI) imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS) to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in remote-sensing classification. View Full-Text
Keywords: classifier ensembles; feature extraction; boosting naïve Bayesian tree; rotation forest; multi-feature imagery classification classifier ensembles; feature extraction; boosting naïve Bayesian tree; rotation forest; multi-feature imagery classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Xiu, Y.; Liu, W.; Yang, W. An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification. Remote Sens. 2017, 9, 1205.

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