## 1 Introduction
[PERSON] et al. [16] used only the thermal infrared hyperspectral imagery of the DFC dataset to map urban land cover. In this method, a support vector machine (SVM) algorithm was first adopted to classify the thermal infrared hyperspectral imagery data, and then a marker-based minimum spanning forest (MMSF) algorithm was applied to increase the accuracy of the less accurately classified land-cover types. [PERSON] et al. [17] used a semi-supervised local discriminant analysis (SLDA) method to extract features from the thermal infrared hyperspectral imagery data, and extracted spectral and texture features from the visible data. After the spatial-spectral feature space was constructed, a cuckoo search optimization algorithm with mixed binary-continuous coding was adopted for feature selection and SVM parameter determination. To sum up, the existing studies have deemed the thermal infrared hyperspectral data to be a multidimensional data input, and they processed the thermal infrared hyperspectral imagery data of the DFC dataset in the traditional way, where only the raw radiance was considered in the classification task. However, they did not extract the emissivity information from the thermal infrared hyperspectral imagery nor utilize it in the classification task. In the proposed method, to make full use of the characteristics of the thermal infrared hyperspectral imagery, the emissivity information is used in the classification task. In addition, to take full advantage of the spatial and spectral features of the visible imagery, a conditional random field (CRF) algorithm fusing spatial, spectral, and emissivity information, namely SSECRF, is proposed to integrate the visible imagery and the thermal infrared hyperspectral imagery. In the SSECRFalgorithm, the spatial, spectral, and emissivity information is modeled by the potential function. The main contributions of the proposed method are as follows. 1. **Multi-feature fusion framework for high-resolution visible imagery. ** In the proposed classification framework, features from the high-resolution visible imagery are extracted for different purposes. The spectral feature provides the fundamental information of the ground-object categories, and the visible difference vegetation index (VDVI) [18] is adopted for its effectiveness in green plant separation. In addition, texture features and object-based features are extracted to utilize the spatial correlation between neighboring pixels. A multi-feature fusion framework for the visible data is proposed to integrate the above features to form a spectral-spatial feature set, to realize the feature representation of the high-resolution visible imagery. 2. **Emissivity retrieval from the thermal infrared hyperspectral imagery. ** The thermal infrared hyperspectral imagery is a potential data source for the identification of man-made objects. In the proposed approach, the emissivity information is retrieved using an automated atmospheric compensation and temperature-emissivity separation (TES) method, called the FLAASH-IR (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes--Infrared) algorithm [19], and it is used to construct the spatial-spectral-emissivity feature set. The feature set is then input to the unary potential term in the SSECRF algorithm, leading to a better classification performance. 3. **Spatial-spectral-emissivity land-cover classification based on conditional random fields (SSECRF). ** The CRF model can incorporate the spatial contextual information in both the labels and observed data. In the proposed SSECRF algorithm, the spatial-spectral feature set from the visible imagery and the emissivity from the thermal infrared hyperspectral imagery are integrated by constructing the energy function to carry out land-cover classification. The spatial-spectral feature and emissivity feature are fused in the unary potential by calculating the probability.