## 4 Experimental Result
### Experiment Setup  
For public datasets, training was conducted at a rate of 10% for trainest and 90% for testset for each label. The trainset and the testset were each randomly selected as different data. As for the hyperspectral data we obtained (hereinafter, YU Paint data), only 50 trainsets were selected from each label. You can see the specific number of trains and test samples in Tables 2-5. For each model, when training each dataset, the batch size was set to 16\(\sim\)64, and the epoch was set to 100\(\sim\)800. The results were compared with the best performance. Our proposed model is implemented in python and pytorch [42]. Pytorch is a python library for implementing deep learning models. It is an efficient library in which various convolution layers, activation functions, loss functions, optimizers and so forth are defined. The results are generated on a PC equipped with a AMD Ryzen Threadripper 1920X with 4 GHz and Nvdia Geforce GTX 1080 ti graphics card. We compared the proposed model with the representative 1D-CNN Hu model [14] and 3D-CNN models (e.g., [PERSON] model [27], [PERSON] model [26], [PERSON] model [25] and [PERSON] model [15]). We use the YU dataset to prove that the proposed model is better than 3D-CNN models in the HSI dataset with less spatial information. The dataset to which random spatial information was applied was trained in two approaches. First, the models were trained and tested with a dataset of random spatial information and, secondly, models were trained with a general dataset and were tested with a dataset of random spatial information. That is, we conducted the experiment in three ways: (1) Learning and testing with a normal dataset; (2) training and testing with a dataset of random spatial information; (3) training with a normal dataset and testing with a dataset of random spatial information. \begin{table}
\begin{tabular}{c c c c c} \hline \# & **Class Name** & **Total** & **Train** & **Test** \\ \hline
1 & Alfafla & 46 & 4 & 42 \\ \hline
2 & Corn-notill & 1428 & 142 & 1286 \\ \hline
3 & Corn-mintill & 830 & 83 & 747 \\ \hline
4 & Corn & 237 & 23 & 214 \\ \hline
5 & Grass-pasture & 483 & 48 & 435 \\ \hline
6 & Grass-trees & 730 & 73 & 657 \\ \hline
7 & Grass-pasture-mowed & 28 & 2 & 26 \\ \hline
8 & Hay-windrowed & 478 & 47 & 431 \\ \hline
9 & Oats & 20 & 2 & 18 \\ \hline
10 & Soybean-notill & 972 & 97 & 875 \\ \hline
11 & Soybean-mintill & 2455 & 245 & 2210 \\ \hline
12 & Soybean-clean & 593 & 59 & 534 \\ \hline
13 & Wheat & 205 & 20 & 185 \\ \hline
14 & Woods & 1265 & 126 & 1139 \\ \hline
15 & Buildings-Grass-Trees-Drives & 386 & 38 & 348 \\ \hline
16 & Stone-Steel-Towers & 93 & 9 & 84 \\ \hline \end{tabular}
\end{table} 
Table 2: Number of train and test sample in the Indian Pines dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Class Name** & **Total** & **Train** & **Test** \\ \hline
1 & Asphalt & 6631 & 663 & 5968 \\ \hline
2 & Meadows & 18,649 & 1864 & 16,785 \\ \hline
3 & Gravel & 2099 & 209 & 1890 \\ \hline
4 & Trees & 3064 & 306 & 2758 \\ \hline
5 & Painted metal sheets & 1345 & 134 & 1211 \\ \hline
6 & Bare Soil & 5029 & 502 & 4527 \\ \hline
7 & Bitumen & 1330 & 133 & 1197 \\ \hline
8 & Self-Blocking Bricks & 3682 & 368 & 3314 \\ \hline
9 & Shadows & 947 & 94 & 853 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of train and test sample in the Pavia University dataset.