Digging More in Neural World: An Efficient Approach for Hyperspectral Image Classification Using Convolutional Neural Network

  • Adnan Iltaf
  • Matee Ullah
  • Junling Shen
  • Zebin Wu
  • Chuancai LiuEmail author
  • Zeeshan Ahmad
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)


Classification of hyperspectral images (HSI) can benefit from deep learning models with deep architecture in remote sensing. In this letter, a novel method based on Convolutional Neural Network (CNN) is proposed for the classification of hyperspectral images. Due to using more spatio-spectral features for the classification of hyperspectral images, the proposed method outperforms the existing state-of-the-art classification techniques. Our proposed method first reduces the dimension of hyperspectral images using Principle component analysis (PCA). The spatial and spectral features are then exploited by a fixed size convolutional filter to generate the combine spatio-spectral feature maps. Finally, these feature maps are fed into a Multi-Layer Perceptron (MLP) classifier that predicts the class of the pixel vector. To validate the effectiveness of our proposed method, computer simulations are conducted using three datasets namely Indian Pines, Salinas and Pavia University and comparisons with existing techniques are made.


CNN Hyperspectral classification PCA Multi-Layer Perceptron (MLP) Remote sensing 



This work is sponsored by the National Natural Science Foundation of China under Grant No. 61373063 and 61373062; the project of Ministry of Industry and Information Technology of China (Grant No. E0310/1112/02-1).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Adnan Iltaf
    • 1
  • Matee Ullah
    • 1
  • Junling Shen
    • 1
  • Zebin Wu
    • 1
  • Chuancai Liu
    • 1
    Email author
  • Zeeshan Ahmad
    • 2
  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjingChina

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