## 4 Experiments
### Datasets  
The ROSIS Pavia University (RPaviaU) scene was captured by the ROSIS HSI sensor over the University of Pavia, Italy. The ROSIS HSI (Reflective Optics System Imaging Spectrometer) sensor was manufactured by the German Aerospace Center (DLR), located in Cologne, Germany. The data cube size of the RPaviaU scene was \(610\times 340\times 103\), where first two dimensions represent the spatial size, while the last dimension is the number of bands. The DAIS Pavia Center (DPaviaC) scene was captured by the DAIS sensor over the center of Pavia City, Italy. The DAIS (Digital Airborne Imaging Spectrometer) sensor was manufactured by the German Aerospace Center (DLR), located in Cologne, Germany. The data cube size of DPaviaC was \(400\times 400\times 72\). There were seven land cover classes shared between RPaviaU and DPaviaC scenes, and the detailed land cover classes as well as the number of labeled samples are listed in Table 1. The data cubes and ground truth maps are illustrated in Figure 4. The white areas in the figure indicate pixels that were not involved in the classification. \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Class Name**} & \multicolumn{2}{c}{**Labeled Samples**} \\ \hline \# & **Name** & **RPaviaU** & **DPaviaC** \\ \hline
1 & Trees & 3064 & 2424 \\ \hline
2 & Asphalt & 6631 & 1704 \\ \hline
3 & Bitumen & 1330 & 685 \\ \hline
4 & Shadows & 947 & 241 \\ \hline
5 & Self-Blocking Bricks & 3682 & 2237 \\ \hline
6 & Meadows & 18,649 & 1251 \\ \hline
7 & Bare Soil & 5029 & 1475 \\ \hline \end{tabular}
\end{table} 
Table 1: Number of labeled samples in each land cover class within the RPaviaU and DPaviaC datasets. The EO-1 Hangzhou (EHangzhou) scene was captured by the EO-1 Hyperion HSI sensor over Hangzhou city, Zhejiang, China. The EO-1 Hyperion HSI sensor was developed by NASA (National Aeronautics and Space Administration), headquartered in Washington, DC, USA. The data cube size of the EHangzhou scene was 590 \(\times\) 230 \(\times\) 198, where the first two dimensions represent the spatial size, while the last dimension is the number of bands. The ROSIS Pavia HR (RPaviaHR) scene was captured by the ROSIS HSI sensor over Pavia city, Italy. The data cube size of the RPaviaHR scene was 1400 \(\times\) 512 \(\times\) 102. There were three common classes between the EHangzhou and RPaviaHR scenes, which were water, ground/building, and vegetation, as shown in Table 2. The data cubes and ground truth maps are illustrated in Figure 5.  
Data preprocessing: In the experiments involving the proposed Hyper-CycleGAN, the dataset was systematically partitioned into training and testing subsets to ensure a rigorous evaluation. Specifically, 150 labeled instances per class were randomly sampled from the source domain for \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Class Name** & \multicolumn{2}{c}{**Labeled Samples**} \\ \hline
**\#** & **Name** & **EHangzhou** & **RPaviaHR** \\ \hline
1 & Water & 18,403 & 22,525 \\ \hline
2 & Ground/Building & 77,450 & 145,416 \\ \hline
3 & Vegetation & 40,207 & 22,961 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of labeled samples in each land cover class within the EHangzhou and RPaviaHR datasets. Figure 4: Datasets along with their ground truths. **(a)** DPaviaU dataset and **(b)** DPaviaC dataset. Figure 5: Datasets along with their ground truths. **(a)** EHangzhou dataset and **(b)** RPaviaHR dataset. training. Similarly, five labeled instances per class were randomly selected from the target domain for training. This random selection process was conducted using stratified sampling to preserve the class distribution. All remaining labeled instances in the target domain, which were not selected for training, were assigned to the test set and reserved solely for performance evaluation. To clarify, \"labeled source samples\" refer to pixels with ground truth labels from the source hyperspectral image, while \"labeled target samples\" refer to pixels with ground truth labels from the target hyperspectral image.