**5. Conclusions**

In this paper, a novel hyperspectral remote sensing image fusion using structure tensor approach is presented. The proposed method is believed to be the first work using the structure tensor to fuse the HS and PAN images. The PAN image is first sharpened by the LOG image enhancement method. Then, structure tensor is applied to the enhanced PAN image to extract the spatial information, while the spatial details of the HS image are obtained by an adaptive weighted method, simultaneously. To obtain the complete spatial details and accomplish spatial consistency, a suitable weighted fusion algorithm is proposed to integrate the extracted spatial details of the HS and PAN images. Experimental results from the Pavia University, Moffett field, Washington DC, and Hyperion datasets have shown that the proposed method is superior to the other fusion methods in retaining the spectral information and improving the spatial quality. In the future, we will investigate the issue of how to determine the weight coefficients *λ*1 and *λ*2 adaptively.

**Acknowledgments:** This work was supported by the National Science Foundation of China under Grants 61222101, 61272120, 61301287, 61301291 and 61350110239.

**Author Contributions:** Jiahui Qu and Yunsong Li devised the approach and analyzed the data. Jiahui Qu, Jie Lei, and Wenqian Dong performed the experiments. Zhiyong Zeng, and Dunyu Chen contributed materials and analysis tools. Jiahui Qu drafted the manuscript, which was revised by all authors. All authors read and approved the submitted manuscript.

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