**6. Conclusions**

In this paper, we present an effective classification framework by integrating the spectral, spatial, and hierarchical structure information into the SVM classifier in a way of multiple kernels. In this framework, the spectral kernel is constructed using directly the original HSI, the spatial kernel is modeled using the EMP method and the hierarchical kernel is introduced by combining the techniques of FMS and AMG-MHSEG. The main advantage of the proposed framework is to utilize spatial structure information in multiple scales for HSI classification. Experimental results on two benchmark HSIs confirmed the following conclusions: (1) The combination of the spectral, spatial and hierarchical kernels in the SVM-SSHK method can generate better classification results than using any single or double of these three kernels; (2) The SVM-SSHK method can achieve the most accurate classification results under different training sets, when compared to the popular kernel-based classification methods. Specifically, SVM-SSHK can be 0.02–15.24% and 0.08–15.61% higher than the other methods in average in the terms of OA for the IP and UP images, respectively; (3) SVM-SSHK can demonstrate stable classification performance in most cases of different parameter settings on the weights of the three kernels. In conclusion, the SVM-SSHK method is very promising for the improvement of classification of hyperspectral images. In the future, advanced studies will be performed by exploring more efficient SVMs with multiple kernels.

**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (61271408). The authors would like to thank D. Landgrebe from Purdue University for providing the AVIRIS image of Indian Pines and the Gamba from University of Pavia for providing the ROSIS data set.

**Author Contributions:** Y.W. and H.D. implemented all the proposed classification method and conducted the experiments. H.D. finished the first draft, Y.W. supervised the research and contributed to the editing and review of the manuscript.

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