Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of…

• NN
  National Natural Science Foundation of China

  Awards: 41201341, NSFC-61302164
• FR
  Fundamental Research Funds for the Central Universities

  Award: YS1404