## 1 Introduction  
Land-cover classification using remote sensing imagery enables us to recognize the physical composition and characteristics of different land-cover types [1; 2]. High-resolution visible imagery has been the commonly used data sources for land-cover classification in recent years [3]. The spatial characteristics of visible images reflect the contextual relationships, including the texture features [4], morphological features [5], and object-based features [6], which are important for the classification task. Nowadays, digital cameras are also mounted on the airplane or unmanned aerial vehicle and are used to capture the visible imagery, which can provide detailed shape and texture information with a fine resolution to distinguish the land-cover categories. However, the high spatial resolution imagery acquired in the visible band may be limited at accurately distinguishing the land-covertypes with similar spectral values, given that the three bands as R, G and B channels cannot provide enough spectral information. Thermal infrared hyperspectral imagery is a very challenging data source with high potential: it records the emissivity and temperature of the ground objects, regardless of the illumination conditions [7]. The emissivity is closely related to the physical properties of ground surface materials, so thermal infrared hyperspectral imagery is an additional data source for land-cover classification, especially in mineral and urban areas. In addition, the spectral features of minerals in the thermal bands are more salient than those in the visible bands [8], so thermal infrared hyperspectral data can contribute to the urban materials mapping [9]. When conducting land-cover classification fusing visible and thermal infrared hyperspectral imagery, it is intuitive to combine the respective advantages of them efficiently to pursue the better land-cover classification results. To encourage the fusion of visible and thermal infrared hyperspectral imagery to conduct urban land-cover classification, a dataset was published in the 2014 edition of the IEEE Geoscience and Remote Sensing Data Fusion Contest (DFC), involving coarse-resolution thermal infrared hyperspectral imagery and fine-resolution visible imagery [10]. Based on this DFC dataset, research has been carried out on fusion-based urban classification. [PERSON] [11] utilized self-organizing maps for semi-supervised learning and visualization of the partially labeled data in order to properly fuse the color and texture features from the visible imagery with the thermal spectra from the thermal infrared hyperspectral imagery. [PERSON] et al. [12] carried out principal component analysis (PCA) transform on the thermal infrared hyperspectral imagery and extracted the gray-level co-occurrence matrix (GLCM), the morphological profile (MP), and the structural feature set (SFS) from the visible data. Finally, they calculated the vegetation index (VI) by calculating the mean value of the thermal infrared hyperspectral imagery bands and the red band of the visible imagery. [PERSON] et al. [13] used image gap inpainting to deal with the swath inconsistency between the thermal infrared hyperspectral imagery and visible imagery, and [PERSON] also developed a road extraction method through fusing the classification result of the thermal infrared hyperspectral imagery and the segmentation result of the visible imagery to improve the overall classification accuracy. [PERSON] et al. [14] proposed a decision-level-based synergetic classification method to improve the overall classification accuracy of the DFC dataset while minimizing the misclassification of roofs and trees at the same time. In addition, feature extraction is an important process to reduce the dimension of the DFC dataset before classification. [PERSON] et al. [15] compared the object-oriented and pixel-based classification approaches on the DFC dataset, and concluded that the object-oriented image analysis technique obtains a high individual user's accuracy for each classified land-cover class.