## 3 Spatial-Spectral-Emissivity Land-Cover Classification Based on Conditional Random Fields (SSECRF)
### Methodology of SSECRF
#### 3.2.2 Multi-Feature Extraction from the Visible Imagery  
In general, the texture, spatial, and object-based features are extracted to complement the spectral information. In this paper, the VDVI image, texture images, spectral feature, and object-based spatial image are used to make up the feature map. 1. Spectral Feature  
Although visible images are lacking in abundant spectral information, they can be used for the identification of objects that have a large difference in spectral properties. The spectral feature is expressed as follows:  
\[F_{spectral}=\left[x_{i}\right]^{T}=\left[x_{i}^{r},x_{i}^{g},x_{i}^{b}\right]^{T} \tag{5}\]  
where \(x_{i}\) is defined as the spectral vector in site \(i\). Figure 3: The proposed Spatial-Spectral-Emissivity Land-Cover Classification Based on Conditional Random Fields (SSERF) algorithm for fusing thermal infrared hyperspectral imagery and visible imagery. Visible Difference Vegetation Index (VDVI)  
In remote sensing applications, there are more than 100 types of vegetation index (VI), but most of the vegetation indices are calculated with the visible and near-infrared bands, including the normalized difference vegetation index (NDVI), the ratio vegetation index (RVI), and the enhanced vegetation index (EVI). Considering the spectral characteristics of visible imagery, [PERSON] et al. (2018) referred to the construction principles and forms of the NDVI and proposed the comprehensive utilization of the red, green, and blue bands to generate the VDVI, which is calculated as follows:  
\[VDVI=\frac{2\times\rho_{green}-(\rho_{red}+\rho_{blue})}{2\times\rho_{green} +(\rho_{red}+\rho_{blue})} \tag{6}\]  
where \(\rho_{green}\), \(\rho_{red}\), and \(\rho_{blue}\) are the digital number or reflectance values in the green, red, and blue bands for visible imagery. 3 Texture Feature  
The texture feature of visible imagery plays a critical role in distinguishing the objects which have similar spectral features. A texture feature which considers the spatial relationship of pixels is the GLCM. The GLCM describes the texture of an image by calculating the frequency of the pixel pairs with specific values and in a specified spatial relationship occurring in the image, creating a GLCM, and then extracting statistical measures from this matrix. A total of 14 kinds of texture features are optional in remote sensing applications. In urban classification, the object shape and material properties of the ground objects are generally considered. The mean reflects the regularity of the texture. If the texture is disorganized and difficult to describe, the value will be small. Contrarily, the texture is regular and easy to describe when the value is larger. The homogeneity measures the local gray uniformity of an image. If the texture of different regions lack of changes and the local gray level of the image is uniform, then the homogeneity will be larger. The contrast directly reflects the brightness contrast of a pixel and its neighboring regions. If the element far from the diagonal has a greater value, i.e., the brightness of this image change quickly, then the contrast will be larger. Meanwhile, the contrast reflects the clarity and texture depth of the image. Therefore, in the proposed multi-feature fusion framework, the texture features of mean, homogeneity, and contrast are obtained to describe the spatial texture feature. The features can be represented as the following forms:  
\[F_{texture\_r},F_{texture\_g},F_{texture\_b}=\left[x_{i}^{mean},x_{i}^{homogeneity},x_{i}^{contrast} \right]^{T} \tag{7}\]  
4 Object-Based Feature  
Visible imagery has abundant spatial, texture, and spectral characteristics. In order to alleviate the effect of the within-class spectral variability and noise in the feature space, a segmentation prior is essential. Generally speaking, object-based feature extraction can be divided into object construction and feature extraction.