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.

翻译：在拟议的SEHybridSN模型中,一个密集的区块被用来重新利用浅色特征,目的是更好地利用等级空间光谱特征,随后的深度可分离的相变层被用来区分空间信息,通过频道注意方法对空间光谱特征作了进一步的改进,该方法在每3D层和2D层后进行。实验结果显示,我们提议的模型利用极少的培训数据学习了更具有歧视性的空间光谱特征。SEHybridSN只使用0.05和0.01标记的培训数据,取得了非常令人满意的成绩。