ORCID Identifiers
Document Type
Article
Source of Publication
PLoS ONE
Publication Date
1-1-2018
Abstract
© 2018 Ahmad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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 from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.
DOI Link
ISSN
Publisher
Public Library of Science
Volume
13
Issue
1
First Page
e0188996
Disciplines
Computer Sciences
Keywords
classification, classifier, human, learning, randomized controlled trial, sampling, fuzzy logic, image enhancement, machine learning, procedures, remote sensing, statistical model, statistics and numerical data, support vector machine, validation study, Fuzzy Logic, Image Enhancement, Machine Learning, Models, Statistical, Remote Sensing Technology, Support Vector Machine
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ahmad, Muhammad; Protasov, Stanislav; Khan, Adil Mehmood; Hussain, Rasheed; Khattak, Asad Masood; and Khan, Wajahat Ali, "Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers" (2018). All Works. 1742.
https://zuscholars.zu.ac.ae/works/1742
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series