Hyperspectral Image Classification Using Multi Vote Strategy on Convolutional Neural Network and Sparse Representation Joint Feature

Daoming Ye, Rong Zhang, Dixiu Xue

Abstract: Classification is one of the most popular topics in hyperspectral image (HSI). This paper proposes a method that uses multi vote strategy on convolutional neural network and sparse representation joint feature in hyperspectral image classification. First, the labeled spectral information was extracted by Principal Component Analysis (PCA) as well as the spatial information, at the same time, we feed the convolutional neural network and sparse representation joint feature to SVM. Then, we use multi-vote strategy to get the final result. Experimental results based on public database demonstrate that the proposed method provides better classification accuracy than previous hyperspectral classification methods.