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A Deep Belief Network for Classifying Remotely-Sensed Hyperspectral Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9474)

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.

Keywords

Deep Belief Network (DBN) Hyperspectral Remote Sensing Data Deep Learning Architecture (DLA) Restricted Boltzmann Machines (RBM) Support Vector Machine Kernel Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was supported by NASA EPSCoR under cooperative agreement No. NNX10AR89A.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of Nevada, Las VegasLas VegasUSA

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