## 4 Experimental Discussion
### Introduction to the Datasets  
Three publicly available hyperspectral datasets were used in this experiment: the Indian Pines dataset, the Pavia University scene dataset, and the Houston 2013 dataset. We will use different models to classify the three datasets. In order to make the samples uniform, we have provided both normalization and standardization as pre-processing steps of the samples.  
Table 2 and Figure 5 present the feature types of the Indian Pines dataset. This dataset was collected by the AVIRIS sensor at the Indian Pines test site in northwestern Indiana. Its image size is \(145\times 145\) pixels and it contains \(224\) spectral bands, of which \(200\) effective spectral bands remain after removing the water absorption band. The scene is composed of two-thirds agriculture and one-third forest or vegetation, with a total of \(16\) feature classes. Its characteristics are a large number of bands and a narrow spectral range. In terms of class distribution, this dataset is relatively concentrated. As a public dataset, it can be obtained  
Figure 4: Schematic diagram of GCN Classification. The figure uses different colors to represent different nodes.  
Table 3 and Figure 6 present the feature types of the Pavia University scene dataset. This dataset was acquired by the ROSIS sensor over Pavia, northern Italy, in 2001, with an image size of 610 \(\times\) 610 pixels. The dataset contains 103 bands with spectral bands ranging from 0.43 \(\upmu\)m to 0.86 \(\upmu\)m, totaling 42,776 sample data points. After removing some of the samples that need to be cancelled, its categories can be roughly divided into nine, mainly focusing on urban materials, water, and vegetation. Compared to other datasets, the Pavia University scene dataset has fewer spectral bands and a large number of samples. In terms of class distribution, this dataset includes both scattered and concentrated data. As a public dataset, it can be obtained from Hyperspectral Remote Sensing Scenes--Grupo de Inteligencia Computacional (GIC) (ehu.eus).  
Table 4 and Figure 7 present the feature types of the Houston 2013 dataset. This dataset was obtained from the University of Houston campus and neighboring urban areas. The dataset was collected by the ITRES CASI-1500 sensor and is 349 \(\times\) 1905 pixels in size with 144 spectral bands ranging from 364 to 1046 nm. The spectral resolution is 10 nm. It was used in the 2013 IEEE GRSS data fusion competition. The dataset we used is the HSI version of the cloud-free case, which can be classified into 15 taxonomically challenging categories. The Houston 2013 dataset's characteristics of high spectral and spatial resolution make it an important data source in the remote sensing field. It has a wide spectral coverage and dispersed categories, posing higher demands on classification models. As a public dataset, it can be obtained from the 2013 IEEE GRSS Data Fusion Contest--Fusion of Hyperspectral and LiDAR Data at the Hyperspectral Image Analysis Lab (uh.edu).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Class** & **Sample** \\ \hline
1 & Healthy grass & 1251 \\
2 & Stressed grass & 1254 \\
3 & Synthetic grass & 697 \\
4 & Trees & 1244 \\
5 & Soil & 1242 \\
6 & Water & 325 \\
7 & Residential & 1268 \\
8 & Commercial & 1244 \\
9 & Road & 1252 \\
10 & Highway & 1227 \\
11 & Railway & 1235 \\
12 & Parking Lot 1 & 1233 \\
13 & Parking Lot 2 & 469 \\
14 & Tennis Court & 428 \\
15 & Running Track & 660 \\ total & - & 15,029 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature types and sample sizes of the Houston 2013 dataset.  
Figure 6: False-color image and ground truth of the Pavia University scene dataset.