Deep Learning-Based Classification of Hyperspectral Data

Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification…

• NA
  National Aeronautics and Space Administration
• NN
  National Natural Science Foundation of China

  Awards: 61371180, 61301206
• FR
  Fundamental Research Funds for the Central Universities

  Award: 2013028