Integration of classifiers for improvement of vegetation category identification accuracy based on image objects

Abstract

Though the multi‐classifier system (MCS) has been used widely in the area of machine learning, it has rarely been used in processing remotely sensed data. To test whether MCS could improve land cover classification results from remote sensing imagery, conservative voting, majority voting, and plurality voting were processed to combine individual classification results in this study. Considering the characteristics of high spatial resolution imagery, object‐oriented classification methods were employed. Firstly, the objects were acquired by segmenting the original images at two scales; then taking the “objects” as the classification units, five kinds of classifiers—iterative self‐organising data analysis (ISODATA), Mahalanobis distance, maximum likelihood, neural network, and expert system—were selected to classify urban vegetation categories using different samples and feature spaces to maintain independence among individual classifiers. The three voting rules were then used to combine the individual classification results. For unclassified objects, the ISODATA method was used again to identify their categories. The accuracy assessment showed that the three combined classification results gained higher accuracy than the individual classifier, and the plurality voting rules obtained the best identification results.