## 2 Materials and Methods  
### Design Goals and Display Strategy  
For the object-oriented method, the oriented classes should first be determined, i.e., real category of objects, interest class oriented, different owners, or unusual class oriented. In this paper, the most basic feature, real category of objects (represented by supervised information), is taken as an example; however, the approaches can also be applied to other features by analogy. Then, optimization and evaluation criteria should be determined according to the needs of the user. Finally, a concrete display strategy for the final output image is determined. Therefore, when the processed data only refer to some specific characteristics or some specific considered supervised information, the amount of data that needs to be processed will decrease by a large margin, and the data feature standards, which need to be ensured, will also tend to be unified. For real airborne remote sensing images with multiple categories, the observer is usually more concerned about how to distinguish different categories and identify the fine texture between pixels. Therefore, the goal of the proposed method is to simultaneously display differences among different object information (supervised information) and the relationship between different pixels (data information), which are mainly evaluated by the separability between classes (\(\lambda\)) and the correlation of the original data (\(\rho\)). Other evaluation criteria are also considered in the display strategy, such as the optimum index factor (OIF) and pixel separability (\(\delta\)). The relevant evaluation criteria will be described in detail in Section 3.2. The flow diagram of the method is shown in Figure 1. First, the data are adjusted according to the supervised information. The useless information is removed, and the remaining pixels are classified according to the supervision information. Second, in order to map all information in three-channel color space, it is necessary to reduce the hyperspectral image with hundreds of bands into three bands. Manifold learning with nonlinear advantages [14] is used as a dimension reduction method for each category in this step. The third band of generated images is denoted as I\({}_{1}\) to represent different categories, the first two bands are denoted as I\({}_{2}\) to represent fine texture, and the pixels of the \(j\)-th class displayed in the color space are denoted as O\({}_{j}\). If the generated image is displayed in the whole color space at this point, it is most commonly the multicategory false-color visualization strategy for hyperspectral imaging. However, if each category is displayed independently, joint display of multiple images is required. To solve this problem, a hue segmentation strategy and a separation factor are used in this method. Hues are divided into areas to represent different categories. The unique hues that represent each category based on the supervised information in the selected color space are determined to create different categories that can be displayed as a whole in one image. After that, a separation factor to adjust the contrast among categories is optional. The saturation and lightness of each pixel in the image are determined by I\({}_{2}\), and I\({}_{1}\) is used to describe tonal fluctuation of each pixel in coordination with a separation factor. Finally, the above data are combined into three dimensions and displayed in a certain color space with supervised information. Figure 1: Flow diagram of the object-oriented visualization method of hyperspectral image (HSI). This method has several advantages for displaying the HSI with supervised information:  
1. It can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. 2.