Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of imbalance between high dimensionality and limited availability of training samples for the classification of HSI, a few strategies such as L2 regularization and dropout are investigated to avoid overfitting in class data…

• NN
  National Natural Science Foundation of China

  Award: 61301206
• FR
  Fundamental Research Funds for the Central Universities

  Award: HIT. NSRIF.2013028