  Deep learning methods have shown considerable potential for hyperspectral
image (HSI) classification, which can achieve high accuracy compared with
traditional methods. However, they often need a large number of training
samples and have a lot of parameters and high computational overhead. To solve
these problems, this paper proposes a new network architecture,
LiteDepthwiseNet, for HSI classification. Based on 3D depthwise convolution,
LiteDepthwiseNet can decompose standard convolution into depthwise convolution
and pointwise convolution, which can achieve high classification performance
with minimal parameters. Moreover, we remove the ReLU layer and Batch
Normalization layer in the original 3D depthwise convolution, which
significantly improves the overfitting phenomenon of the model on small sized
datasets. In addition, focal loss is used as the loss function to improve the
model's attention on difficult samples and unbalanced data, and its training
performance is significantly better than that of cross-entropy loss or balanced
cross-entropy loss. Experiment results on three benchmark hyperspectral
datasets show that LiteDepthwiseNet achieves state-of-the-art performance with
a very small number of parameters and low computational cost.
