A Two-Branch Network with Semi-Supervised Learning for Hyperspectral Classification

In order to promote progress on fusion and analysis methodologies for multi-source remote sensing data, The Image Analysis and Data Fusion Technical Committee organized the 2018 IEEE GRSS Data Fusion contest. In this contest, we proposed a two-branch convolution network for hyperspectral image classification with a data re-sampling strategy and semi-supervised learning to address three existing problems, i.e. multi-scale feature learning, data imbalance, and small size of the dataset. The contest showed that our proposal achieved the best performance on two metrics: the overall accuracy of 77.39% and a kappa coefficient of 0.76 on the hyperspectral images provided by 2018 IEEE GRSS Data Fusion Contest.