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Incorporating disparate features from multiple sources can provide valuable diverse information for remote sensing data analysis. However, multisource remote sensing data require large quantities of labeled data to train robust super-vised classifiers, which are often difficult and expensive to acquire.
A mixture-of-kernel approach can facilitate the construction of an effective formulation for acquiring useful samples via active learning (AL). In this paper, we propose an ensemble multiple kernel active learning (EnsembleMKL-AL) framework that incor-porates different types of features extracted from multisensor remote sensing data (hyperspectral imagery and LiDAR data) for robust classification. An ensemble of probabilistic multiple kernel classifiers is embedded into a maximum disagreement-based AL system, which adaptively optimizes the kernel for each source during the AL process. At the end of each learning step, a decision fusion strategy is implemented to make a final decision based on the probabilistic outputs. The proposed framework is tested in a multisource environment, including different types of features extracted from hyperspectral and LiDAR data. The experimental results validate the efficacy of the proposed approach. In addition, we demonstrate that using ensemble classifiers and a large number of disparate but relevant features can further improve the performance of an AL-based classification approach.
The authors would like to thank the IEEE GRSS Data Fusion Technical Committee for providing the UH multisensor datasets, the NSF Funded National Center for Airborne Laser Mapping (NCALM) at the University of Houston for acquiring the data, and X. Zhou, M. Cui, A. Singhania, and J. Fernandez for preprocessing the data.
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 D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 606–617,Jun. 2011.
 M. Crawford, D. Tuia, and H. Yang, “Active learning: Any value for classification of remotely sensed data?” in Proc. IEEE, vol. 101, no. 3, pp. 593–608, Mar. 2013.
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