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.
CITATION STYLE
Ye, D., Zhang, R., & Xue, D. (2017). Hyperspectral image classification using multi vote strategy on convolutional neural network and sparse representation joint feature. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10668 LNCS, pp. 337–346). Springer Verlag. https://doi.org/10.1007/978-3-319-71598-8_30
Mendeley helps you to discover research relevant for your work.