  Recently, hyperspectral image (HSI) classification approaches based on deep
learning (DL) models have been proposed and shown promising performance.
However, because of very limited available training samples and massive model
parameters, DL methods may suffer from overfitting. In this paper, we propose
an end-to-end 3-D lightweight convolutional neural network (CNN) (abbreviated
as 3-D-LWNet) for limited samples-based HSI classification. Compared with
conventional 3-D-CNN models, the proposed 3-D-LWNet has a deeper network
structure, less parameters, and lower computation cost, resulting in better
classification performance. To further alleviate the small sample problem, we
also propose two transfer learning strategies: 1) cross-sensor strategy, in
which we pretrain a 3-D model in the source HSI data sets containing a greater
number of labeled samples and then transfer it to the target HSI data sets and
2) cross-modal strategy, in which we pretrain a 3-D model in the 2-D RGB image
data sets containing a large number of samples and then transfer it to the
target HSI data sets. In contrast to previous approaches, we do not impose
restrictions over the source data sets, in which they do not have to be
collected by the same sensors as the target data sets. Experiments on three
public HSI data sets captured by different sensors demonstrate that our model
achieves competitive performance for HSI classification compared to several
state-of-the-art methods
