## 4 Establishment of Simulated HSI Dataset Based on Experimental Measurement
### Establishment of HSI Dataset  
The above shows the simulation of mixed cells and the effect of noise on the spectrum. The following will introduce the process and ideas of designing the dataset. Figure 7 shows the simulation process of HSIs of objects with similar colors in this paper: the spectral reflectance of the measured ground objects is taken as the spectral information for pure pixels, and the gradually changing abundance is set to calculate the mixed pixels, so that the mixed pixels of different abundances in the image can be obtained. After setting different SNRs and labeling the samples, the hyperspectral simulation images of objects with close spectral characteristics can be obtained for the test of DR results. The simulation of HSIs can be performed with a high degree of flexibility by establishing pixel and noise controls. Figure 6: Mixed reflectivity of pixel \(i\) and pixel \(j\). Figure 7: Flexible HSI simulation of ground objects. Figure 8 is a schematic diagram of the spatial location of the hyperspectral dataset for 4 types of ground objects, in which the clove leaves, shrub leaves and grasses are classified into one class, and the black area is the blank position of the data. This paper sets 10 abundance ratios: [0.75, 0.70, 0.65, 0.60, 0.55, 0.45, 0.40, 0.35, 0.30, 0.25]. Table 1 shows the meaning of the labels in the spatial schematic diagram. This paper adopts a single-label classification strategy. Mixed cells are also treated as pure cells and assigned a single label for classification, and the object class with greater abundance in the mixed pixel is used as the label of the pixel. Pixel attributes: P represents a pure pixel, followed by numbers representing the feature class label. M represents a mixed pixel; the first number of the mixed pixel represents the feature class with the pixel abundance greater than 0.5. For example, M21 represents a mixed pixel of green coating and vegetation, and the coating abundance is greater than 0.5. Figure 9 corresponds to Table 1, which shows the spectral characteristics of the pixels in the hyperspectral simulation image (SNR = 1000). Each subgraph in Figure 9 consists of a pure pixel randomly selected from each of the two ground objects and their mixed pixels with different abundances. In the figures, different colors represent different classes of ground object pixels, the solid lines represent the pure pixel spectrum, and the dashed lines represent the mixed pixel spectrum. \begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Ground Object** & **Dominant Object 1** & **Pixel Attribute** & **Samples Size** \\ \hline C1 & Vegetation & Vegetation & P1 & 720 \\ C2 & Green coating & Green coating & P2 & 240 \\ C3 & Artificial leaf & Artificial leaf & P3 & 240 \\ C4 & Camouflage cloth & Camouflage cloth & P4 & 240 \\ C2 & Vegetation-Coating & Green coating & M21 & 120 \\ C1 & Vegetation-Coating & Vegetation & M12 & 120 \\ C3 & Vegetation-Artificial leaf & Artificial leaf & M31 & 120 \\ C1 & Vegetation-Artificial leaf & Vegetation & M13 & 120 \\ C4 & Vegetation-Camouflage cloth & Camouflage cloth & M41 & 120 \\ C1 & Vegetation-Camouflage cloth & Vegetation & M14 & 120 \\ C2 & Coating-Artificial leaf & Green coating & M23 & 120 \\ C3 & Coating-Artificial leaf & Artificial leaf & M32 & 120 \\ C3 & Artificial leaf-Camouflage cloth & Artificial leaf & M34 & 120 \\ C4 & Artificial leaf-Camouflage cloth & Camouflage cloth & M43 & 120 \\ C4 & Camouflage cloth-Coating & Camouflage cloth & M42 & 120 \\ C2 & Camouflage Clot-Coating & Green coating & M24 & 120 \\ \hline \end{tabular}  
* Dominant object represents an object with greater abundance in a mixed pixel.  
\end{table} 
Table 1: The meaning of the symbols in the space diagram. Figure 8: Simulation data label diagram (Ground Truth).