Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network

Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral-spatial FE and classification…

• NN
  National Natural Science Foundation of China

  Awards: 61371180, 61471148, 61301206
• FR
  Fundamental Research Funds for the Central Universities

  Award: HIT. NSRIF.2013028