Multimedia Tools and Applications

, Volume 77, Issue 20, pp 27061–27074 | Cite as

Efficient classification of the hyperspectral images using deep learning

  • Simranjit SinghEmail author
  • Singara Singh Kasana


Classification techniques applicable to the hyperspectral images do not extract deep features from the hyperspectral image efficiently. In this work, a deep learning approach is proposed to extract the deep features, and these features are utilized to propose a novel framework for classification of the hyperspectral image. The framework uses LPP, DCNN and logistic regression. Data of a hyperspectral image is processed by LPP for dimensionality reduction as it contains a large number of dimensions. Afterward, a DCNN is constructed with Autoencoders which is then passed to the logistic regression for classification. Proposed framework is tested on Indian Pines and Salinas data sets. High accuracy is achieved using the proposed framework in comparison of existing machine learning models.


Auto Encoders LPP DCNN HSI Neural networks PCA SVM 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar Institute of Engineering and TechnologyPatialaIndia

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