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Open AccessFeature PaperArticle

A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification

1
School of Surveying and Geospatial Engineering, University of Tehran, Tehran 1417466191, Iran
2
Center Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, QC G1K 9A9, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 297; https://doi.org/10.3390/rs12020297
Received: 25 November 2019 / Revised: 10 January 2020 / Accepted: 14 January 2020 / Published: 16 January 2020
(This article belongs to the Special Issue Advanced Machine Learning Approaches for Hyperspectral Data Analysis)
This paper introduces a novel multi-view multi-learner (MVML) active learning method, in which the different views are generated by a genetic algorithm (GA). The GA-based view generation method attempts to construct diverse, sufficient, and independent views by considering both inter- and intra-view confidences. Hyperspectral data inherently owns high dimensionality, which makes it suitable for multi-view learning algorithms. Furthermore, by employing multiple learners at each view, a more accurate estimation of the underlying data distribution can be obtained. We also implemented a spectral-spatial graph-based semi-supervised learning (SSL) method as the classifier, which improved the performance of the classification task in comparison with supervised learning. The evaluation of the proposed method was based on three different benchmark hyperspectral data sets. The results were also compared with other state-of-the-art AL-SSL methods. The experimental results demonstrated the efficiency and statistically significant superiority of the proposed method. The GA-MVML AL method improved the classification performances by 16.68%, 18.37%, and 15.1% for different data sets after 40 iterations. View Full-Text
Keywords: active learning (AL); multi-view learning; multi-learner learning; multi-view multi-learner (MVML); genetic algorithms (GA); view generation; hyperspectral image classification active learning (AL); multi-view learning; multi-learner learning; multi-view multi-learner (MVML); genetic algorithms (GA); view generation; hyperspectral image classification
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MDPI and ACS Style

Jamshidpour, N.; Safari, A.; Homayouni, S. A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification. Remote Sens. 2020, 12, 297.

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