Secondary Classification on One-against-Rest Multiclassification Structure

Abstract

One-against-rest (OAR) is a well known multiclassification structure, which is an extension from binary classifiers. It has shown its great potential in pattern recognition and hyperspectral data processing. However, existence of unclassified region limits its application. In this paper, a new multiclassifier based on OAR combined with one-against-one (OAO) structure is proposed. In the multiclassifier, OAO is used to classify the unclassified region to improve performance of OAR. At the same time, the formation of unclassified region is discussed, and the pattern of selecting classifiers for secondary classification on unclassified region is proposed. To compare secondary classifiers and prove the conclusion, other six classifiers are selected , which are decision tree (DT), minimum distance (MD) based on Euclidean distance, MD based on Euclidean distance with kernel function, MD based on Mahalanobis distance, spectral mapping classifier (SMC) and maximum likelihood classifier (MLC). The SVM is used for OAR, OAO and DT in experiment and a hyperspectral remote sensing image is used as testing samples.