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.
Highlights
Updated land cover data provide useful information for multi-temporal studies and are required inputs for land cover change models, climate change models or post-catastrophe analysis [1,2]
Satisfactory classification results depend on basic data with little noise and on a classification method that performs well
harmonic analysis of time series (HANTS) processing was applied for Enhanced Vegetation Index (EVI) preprocessing to increase the separability between land classes and help to improve classification accuracy
Summary
Updated land cover data provide useful information for multi-temporal studies and are required inputs for land cover change models, climate change models or post-catastrophe analysis [1,2]. Benefits of high-temporal frequency, remote-sensing imagery include the unique opportunity for acquiring land cover information through the process of imagery interpretation and classification [3]. To generate updated land cover data at different scales, researchers have proposed a series of remote sensing imagery classification techniques. Using the individual pixel as the basic analytical unit, the techniques can be grouped into one of three categories: unsupervised classification methods (i.e., ISODATA and K-means), supervised classification methods As the spatial resolution increases quickly, object-based classification methods are proposed to address high-resolution remote-sensing images. In these methods, the pixels with homogeneous properties are grouped into basic units instead of individual pixels and the spatial contextual information is considered [6,7,8]
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