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Optimal Parameter Selection in Hyperspectral Classification Based on Convolutional Neural Network

Authors :
Qiaoqiao Sun
Xuefeng Liu
Salah Bourennane
Bourennane, Salah
GSM (GSM)
Institut FRESNEL (FRESNEL)
Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)
Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)
Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)
Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)
Source :
International Conference on Frontiers of Signal Processing, International Conference on Frontiers of Signal Processing, 2019, Marseille, France
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

Classification is a key technique in hyperspectral image (HSI) applications. Deep learning algorithms, which exhibit strong modeling and representational capabilities, have been successfully adopted in fields such as image and language processing. And convolutional neural networks (CNNs) have been used for HSI classification and some interesting results have been obtained. Owing to local connection and weight sharing, the number of parameters is reduced to some extent, but there are still many parameters and the deeper the network, the larger is the number of parameters. The network performance is strongly influenced by the parameter settings. To obtain the optimal CNN parameters for HSI classification, this paper proposes a classification method based on a CNN with parameter tuning (CNN-PT). The network parameters are tuned in turn according to the unique variable principle. Simulation results show that the proposed CNN-PT method has considerable potential for HSI classification compared to previous methods.

Details

Language :
English
Database :
OpenAIRE
Journal :
International Conference on Frontiers of Signal Processing, International Conference on Frontiers of Signal Processing, 2019, Marseille, France
Accession number :
edsair.doi.dedup.....1191b4c2d0e364ca6fa8d93c6b0a1bf6