Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

ABSTRACT: Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances f

SUBMITTER: Ahmad M 

PROVIDER: S-EPMC5756090 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature