## 2 Research Area and Data
### Hyperspectral Data Source for the Research Area
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Name** & **Train.** & **Val.** & **Test.** \\ \hline
1 & Red roof & 702 & 702 & 12,637 \\
2 & Road & 176 & 175 & 3161 \\  & Bare soil & 1091 & 1091 & 19,639 \\
4 & Cotton & 8164 & 8164 & 146,957 \\
5 & Cotton firewood & 311 & 311 & 5596 \\
6 & Rape & 2228 & 2228 & 40,101 \\
7 & Chinese cabbage & 1205 & 1205 & 21,693 \\
5 & Pakchoi & 202 & 203 & 3649 \\
9 & Cabbage & 541 & 541 & 9737 \\
10 & Tuber mustard & 619 & 620 & 11,155 \\
11 & Brassica parachinensis & 551 & 551 & 9913 \\
12 & Brassica chinensis & 447 & 448 & 8059 \\  & Small Brassica chinensis & 1125 & 1126 & 20,256 \\
14 & Lactuca sativa & 368 & 368 & 6620 \\
15 & Celtuce & 50 & 50 & 902 \\
16 & Film covered lettuce & 363 & 363 & 6536 \\
17 & Romaine lettuce & 151 & 150 & 2709 \\
18 & Carrot & 161 & 161 & 2895 \\
19 & White radish & 435 & 436 & 7841 \\
20 & Garlic sprout & 175 & 174 & 3137 \\
21 & Broad bean & 67 & 66 & 1195 \\
22 & Tree & 202 & 202 & 3636 \\ \hline \hline \multicolumn{4}{c}{Total} & 19,334 & 19,335 & 348,024 \\ \hline \end{tabular}
\end{table} 
Table 4: Detailed breakdown of the labeled sample distribution for the WHU-Hi-HongHu dataset. (6) WHU-Hi-LongKou:The WHU-Hi-LongKou dataset was gathered between 13:49 and 14:37 on 17 July 2018, in Longkou, Hubei Province, China. The imagery was captured using a DJI Matrice 600 Pro drone, outfitted with an 8 mm focal length Headwall Nano-Hyperspec sensor. The data collection occurred under clear skies, with temperatures reaching around 36 \({}^{\circ}\)C and a relative humidity of approximately 65%. The research zone was characterized by a straightforward agricultural setting, featuring six crop varieties: corn, cotton, sesame, broad-leaf soybeans, narrow-leaf soybeans, and rice. The drone was operated at a height of 500 m, resulting in images with dimensions of 550 by 400 pixels and comprising 270 spectral bands from 400 to 1000 nm. The spatial resolution of the hyperspectral imagery acquired by the drone was roughly 0.463 m. This dataset is shown in Table 6 below. In the current field of data science and machine learning, the rational division of datasets is crucial for the training and evaluation of models. Our dataset partitioning strategy fully considers the quality and representativeness of the samples to ensure that the model possesses strong generalization capabilities in practical applications. During the model training process, we are well aware of the impact of the quality and representativeness of the training samples on model performance. To enhance the model's generalization ability, we meticulously selected samples of high quality and broad representativeness as the training and validation sets. At the same time, we ensured that the number of samples in the test set far exceeds that of the training/validation sets to fully evaluate the model's performance on unseen data. The advantage of this data partitioning method lies in two \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Name** & **Train.** & **Val.** & **Test.** \\ \hline
1 & Corn & 1725 & 1726 & 31,060 \\
2 & Cotton & 418 & 419 & 31,060 \\
3 & Sesame & 152 & 151 & 2728 \\
4 & Broad-leaf soybean & 3160 & 3161 & 56,891 \\
5 & Narrow-leaf soybean & 208 & 207 & 3736 \\
1 & Rice & 593 & 592 & 10,669 \\
7 & Water & 3353 & 3353 & 60,350 \\
8 & Roads and houses & 356 & 357 & 6411 \\
9 & Mixed weed & 262 & 261 & 4706 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detailed breakdown of the labeled sample distribution for the WHU-Hi-LongKou dataset.