29 November 2017 Multiple-instance ensemble learning for hyperspectral images
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An ensemble framework for multiple-instance (MI) learning (MIL) is introduced for use in hyperspectral images (HSIs) by inspiring the bagging (bootstrap aggregation) method in ensemble learning. Ensemble-based bagging is performed by a small percentage of training samples, and MI bags are formed by a local windowing process with variable window sizes on selected instances. In addition to bootstrap aggregation, random subspace is another method used to diversify base classifiers. The proposed method is implemented using four MIL classification algorithms. The classifier model learning phase is carried out with MI bags, and the estimation phase is performed over single-test instances. In the experimental part of the study, two different HSIs that have ground-truth information are used, and comparative results are demonstrated with state-of-the-art classification methods. In general, the MI ensemble approach produces more compact results in terms of both diversity and error compared to equipollent non-MIL algorithms.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Ugur Ergul, Ugur Ergul, Gokhan Bilgin, Gokhan Bilgin, } "Multiple-instance ensemble learning for hyperspectral images," Journal of Applied Remote Sensing 11(4), 045009 (29 November 2017). https://doi.org/10.1117/1.JRS.11.045009 . Submission: Received: 7 August 2017; Accepted: 7 November 2017
Received: 7 August 2017; Accepted: 7 November 2017; Published: 29 November 2017

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