**6. Conclusions**

In this paper, we investigate a novel hyperspectral classification framework based on an optimal DBN algorithm. In our proposed framework, we develop a new TFE algorithm that employs multi-texture features and band grouping method. The resulting classification framework can offer better classification accuracy than other classic algorithms. To further test our proposed TFE algorithm, a series of experiments based on the combination of the state-of-the-art algorithms and the TFE algorithm are applied on the three classic hyperspectral datasets. Experimental results demonstrate

that the algorithms with TFE outperform those without TFE, which implies that our proposed TFE can play an important role in improving hyperspectral classification performance. We believe that the proposed hyperspectral classification framework based on the optimal DBN and TFE is more suitable to process hyperspectral data in practical applications when training samples are limited.

**Acknowledgments:** This work was partially supported by the National Nature Science Foundation of China (Nos. 61571345, 91538101, 61501346, 61502367 and 61701360) and the 111 project (B08038). It was also partially supported by the Fundamental Research Funds for the Central Universities JB170109, the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2016JQ6023) and General Financial Grant from the China Postdoctoral Science Foundation (No. 2017M623124).

**Author Contributions:** L.J. and L.Y.S. conceived and designed the study; L.J. performed the experiments; X.B. analyzed the data; L.J. and X.B. wrote the paper; and W.K.Y. and D.Q. reviewed and edited the manuscript. All authors read and approved the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
