**5. Conclusions**

CNN-based methods for scene classification are restricted by the computational challenge and limited ability to extract discriminative features. In this paper, the lifting scheme is introduced into deep learning and a lifting scheme-based deep neural network (LSNet) is proposed for remote sensing scene classification. The innovation of this approach lies in its capability to introduce nonlinearity into the feature extraction module to extend the feature space. The lifting scheme is an efficient algorithm for the wavelet transform to fit on the hardware platforms, which shows the potential of LSNet to ease the computational burden. Experiments on the AID datasets demonstrate that LSNet-1d and LSNet-2d are superior to ResNet-1d and ResNet-2d by 2.05% and 0.45%, respectively. The method proposed in this paper has room for further improvement, and we will introduce other nonlinear wavelets into neural networks and implant LSNet into some computationally limited platforms in the future.

**Author Contributions:** Methodology, C.H. and Z.S.; software, T.Q.; writing—original draft preparation, Z.S.; writing—review and editing, C.H.; supervision, D.W. and M.L.

**Funding:** This research was funded by the National Key Research and Development Program of China (No. 2016YFC0803000), the National Natural Science Foundation of China (No. 41371342 and No. 61331016), and the Hubei Innovation Group (2018CFA006).

**Conflicts of Interest:** The authors declare no conflict of interest.
