Using phenological metrics and the multiple classifier fusion method to map land cover types

Abstract

Feature selection and multiple classifier fusion (MCF) are effective approaches to improve land cover classification accuracy. In this study, we combined phenological metrics and the MCF method to map land cover types in Jiangsu province of China during the second crop growing season using moderate resolution imaging spectroradiometer time-series data. Eight phenological metrics were developed and calculated, and a MCF scheme was proposed by combining a simple majority vote and the measurement of posterior probabilities. The four base classifiers (i.e., the maximum likelihood classifier, the Mahalanobis distance classifier, the support vector machine classifier, and the neural networks classifier) and the MCF method were used in classifications using two spectral indices from the original satellite data (direct classification) and the computed metric data (metrics-based classification). Accuracy assessments indicated that the overall accuracies and kappa coefficients of the metrics-based classifications were all higher than those of direct classifications. The average overall accuracy and kappa coefficient of metrics-based classifications were 8.36% and 0.1 higher than that of direct classifications, respectively. Similarly, the overall accuracy and kappa coefficient of MCF generally were close to or exceeded the highest accuracy among all the base classifiers. The highest overall accuracy and kappa coefficient was achieved by classification with the MCF method based on phenological metrics (m-MCF), which were 88% and 0.85, respectively. Our results suggested that combining phenological metrics and MCF in classification is a promising method for land cover mapping in regions where strong phenological signals can be detected.