Abstract
Improving the classification accuracy of remotely sensed data is of paramount interest for science and defense applications. In this paper, we investigate deep learning architectures (DLAs), whose popularity has grown recently due to the discovery of efficient algorithms to train them, one of which, unsupervised pre-training, seeks to initialize the learned model in a way that greatly encourages efficient supervised learning. We propose a structure for a DLA, the deep belief network (DBN), suitable for the classification of remotely-sensed hyperspectral data. To arrive at this structure, we first study the role of the DBN’s width and the duration of pre-training in the learning of features used for the multiclass discrimination of spectral data. We then study the effect of exploiting joint spectral-spatial information. The support vector machine (SVM) is used as a baseline to determine that the proposed method is feasible, offering consistently high classification accuracies in comparison.
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Acknowledgements
This research was supported by NASA EPSCoR under cooperative agreement No. NNX10AR89A.
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Le, J.H., Yazdanpanah, A.P., Regentova, E.E., Muthukumar, V. (2015). A Deep Belief Network for Classifying Remotely-Sensed Hyperspectral Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_61
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DOI: https://doi.org/10.1007/978-3-319-27857-5_61
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