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Journal of Sensors
Volume 2016 (2016), Article ID 3150632, 8 pages
http://dx.doi.org/10.1155/2016/3150632
Research Article

Deep Learning for Hyperspectral Data Classification through Exponential Momentum Deep Convolution Neural Networks

Qi Yue1,2,3 and Caiwen Ma1

1Xi’an Institute of Optics and Precision Mechanics, CAS, Xi’an 710119, China
2University of Chinese Academy of Sciences, Beijing 100039, China
3Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Received 14 June 2016; Revised 20 September 2016; Accepted 4 October 2016

Academic Editor: Biswajeet Pradhan

Copyright © 2016 Qi Yue and Caiwen Ma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Classification is a hot topic in hyperspectral remote sensing community. In the last decades, numerous efforts have been concentrated on the classification problem. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on complex handcrafted features. However, it is rarely known which features are important for the problem. In this paper, a new classification skeleton based on deep machine learning is proposed for hyperspectral data. The proposed classification framework, which is composed of exponential momentum deep convolution neural network and support vector machine (SVM), can hierarchically construct high-level spectral-spatial features in an automated way. Experimental results and quantitative validation on widely used datasets showcase the potential of the developed approach for accurate hyperspectral data classification.