## 5 Conclusions  
In this paper, the SSECRF algorithm has been proposed to efficiently integrate visible imagery and thermal infrared hyperspectral imagery for land-cover classification. The existing methods of fusing the visible and thermal infrared hyperspectral imagery do not take into account the emissivity information, and only the raw radiance information of the thermal infrared hyperspectral imagery is employed. The SSECRF algorithm considers the spatial-contextual information and spectral variability, and makes full use of the emissivity to improve the final classification result. Firstly, features extracted from these two types of imagery are used to fuse the SSE feature set. The unary potential and the pairwise potential are then modeled as the SSE energy function. The unary potential is modeled to integrate the emissivity information with the spatial-spectral feature set from the visible imagery, and the pairwise potential is modeled using a spatial smoothing term to make full use of the spatial-contextual information. Two parameters \(\lambda\) and \(\theta\) have an influence on the pairwise potential term, and the choice of parameters directly determines the classification results.  
The experiments undertaken in this study confirmed that the proposed SSECRF algorithm can efficiently integrate the visible and thermal infrared hyperspectral data, and a satisfactory classification map can be obtained, where the spatial, spectral, and emissivity information are all considered to obtain a better classification result. The final classification map shows a good performance and OA, especially for certain classes, such as roads and bare soil, proving that the SSECRF algorithm is effective.  
In the future, strategies will be investigated that could be applied in distinguishing the vegetation and roofs class more accurately. Also, the temperature information retrieved from the thermal infrared hyperspectral imagery will be also considered.  
**Acknowledgments:** This work was supported by National Natural Science Foundation of China under Grant Nos. 41622107, 41771385 and 41371344, National Key Research and Development Program of China under Grant No. 2017 YFB0504202, and Natural Science Foundation of Hubei Province in China under Grant No. 2016 CFA029. The authors would like to thank Telops Inc. (Quebec City, Quebec, Canada) for acquiring and providing the data used in this study, the IEEE GRS Image Analysis and Data Fusion Technical Committee and [PERSON] (Signal and Image Centre, Royal Military Academy, Belgium) for organizing the 2014 Data Fusion Contest, the Centre de Recherche Public [PERSON] (CRPGL, Luxembourg) and [PERSON] (CRPGL) for their contribution of the Hyper-Cam LWIR sensor, and [PERSON] (University of Genoa, Genoa, Italy) for her contribution in data preparation. The authors also want to thank Telops Inc. (Quebec City, Quebec, Canada) for providing the emissivity map retrieved using Spectral Sciences Inc.'s FLAASH-IR in our experiment.  
**Author Contributions:** All the authors made significant contributions to the work. [PERSON], [PERSON] and [PERSON] designed the research and analyzed the results. [PERSON] and [PERSON] provided advice for the preparation and revision of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.