  In the proposed SEHybridSN model, a dense block was used to reuse shallow
feature and aimed at better exploiting hierarchical spatial spectral feature.
Subsequent depth separable convolutional layers were used to discriminate the
spatial information. Further refinement of spatial spectral features was
realized by the channel attention method, which were performed behind every 3D
convolutional layer and every 2D convolutional layer. Experiment results
indicate that our proposed model learn more discriminative spatial spectral
features using very few training data. SEHybridSN using only 0.05 and 0.01
labeled data for training, a very satisfactory performance is obtained.
