Supervised land cover classification based on the locally reduced convex hull approach

摘要

A novel supervised learning approach, called the locally reduced convex hull (LRCH), is proposed for land cover classification. The method described is capable of increasing the class separability and the representational capacity of the training set, which leads to its high generalization ability in applications. The effectiveness of the LRCH is demonstrated on the classification problem of a multi-spectral data set. In experiments, the LRCH was compared with six common classifiers. Statistical results in terms of the overall accuracy, the Kappa coefficient and McNemar's test show that LRCH outperforms most of the other approaches, with a speed that is comparable to all of them.