AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral Image Classification

  • Heena Patel
  • Kishor P. UplaEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1019)


This paper addresses an approach for classification of hyperspectral imagery (HSI). In remote sensing, the HSI sensor acquires hundreds of images with very narrow but continuous spectral width in visible and near-infrared regions of the electromagnetic (EM) spectrum. Due to the nature of data acquisition with contiguous bands, the HS images are very useful in classification and/or the identification of materials present in the captured geographical area. However, the low spatial resolution and large volume of HS images make the classification of those images as a challenging task. In the proposed approach, we use an autoencoder with convolutional neural network (AECNN) for classification of HS image. Pre-processing procedure with autoencoder leads to obtain optimized weights in the initial layer of CNN model. Moreover, features are enhanced in the HS images by utilizing the autoencoder. The CNN is used for efficient extraction of the features and same is also utilised for the classification of HS data. The potential of the proposed approach has been verified by conducting the experiments on three recent datasets. The experimental results are compared with the results obtained in the geoscience and remote sensing society (GRSS) Image Fusion Contest-2018 held at IEEE International Geoscience and Remote Sensing Symposium (IGARSS)-2018 and other existing frameworks for HSI classification. The testing accuracy of classification for the proposed approach is better than that of the other existing deep learning based methods.


Autoencoder CNN Feature extraction Hyperspectral classification 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Sardar Vallabhbhai National Institute of TechnologySuratIndia

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