**5. Conclusions**

In this paper, a novel multiscale union region adaptive sparse representation, the MURASR, which uses union region integrating patch and superpixel to exploit the spatial information, is proposed for spectral-spatial HSI classification. Unlike the patch region based MASR, the proposed MURASR extends the patch region to the union region. The union region utilizes the integration of the observation that neighboring pixels that belong to the same material usually are strongly correlated with each other and pixels in the superpixel usually belong to the same material. Before sparse representation, multiscale union regions are generated via the union operation for patch and superpixel. Then multiscale adaptive sparse representation is adopted to classify multiscale union regions and an effective probability majority voting method is applied to generate the final result. Experiments on three HSIs demonstrate that the union region based algorithms always perform better than patch region based algorithms and the proposed MURASR outperforms other algorithms in terms of quantitative metrics and visual quality for the classification maps.

As the MURASR is a pixel-based algorithm, if we replace the superpixel with a region growing up from each test pixel, the generated union region will have more accurate representation of the spatial information. Thus, the further research will generate one superpixel for each test pixel. In addition, the structure dictionary for sparse representation is constructed directly by selected training pixels. A trained structure dictionary may decrease the running time of the algorithm and provide more accurate representation for test pixels.

**Acknowledgments:** This research was supported by the National Natural Science Foundation of China under Grant No. 41171339 and 61501413. The authors would like to thank Ly. Fang for providing the source code of MASR and M.-Y. Liu for over-segmentation methods on their website (http://www.escience.cn/people/ LeyuanFang/index.html, http://mingyuliu.net). The authors would like to thank David A. Landgrebe from Purdue University for providing the AVIRIS image of Indian Pines and Paolo Gamba from University of Pavia for providing the ROSIS data set. The authors would like to thank the National Aeronautics and Space Administration Jet Propulsion Laboratory for providing the AVIRIS image of Salinas. The authors would also like to thank the handling editor and anonymous reviewers for their valuable comments and suggestions, which significantly improved the quality of this paper.

**Author Contributions:** Fei Tong and Hengjian Tong proposed the model and implemented the experiments. Fei Tong wrote the manuscript. Junjun Jiang provided overall guidance of the work and edited the manuscript. Yun Zhang reviewed and edited the manuscript.

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