## 2 Methods
### Dataset and Evaluation Metrics  
#### 2.3.1 Dataset Description  
The performance of the classification has been evaluated by comparative experiments on four datasets, namely the University of Houston (UH), Indian Pines (IP), MUUFL, and Trento datasets. The University of Houston (UH) conducted research published by the IEEE Geoscience and Remote Sensing Society. The dataset used by the UH was gathered in 2013 and involved the deployment of the Compact Airborne Spectrographic Imager (CASI). Each image in the dataset comprises 340 \(\times\) 1905 pixels, encompassing 144 distinct spectral bands, covering a wavelength range spanning from 0.38 to 1.05 \(\upmu\)m. The spatial resolution is set at 2.5 m per pixel (MPP). The ground truth for the images comprises 15 unique classes that correspond to various land cover types. The dataset is divided into testing and partitioning sets for each of the 15 classes. For a more comprehensive breakdown of the separate sequences and testing samples, generated by the methodology outlined in the work of [17], for each land cover type, see Table 1.  
Figure 3: The structure of the proposed BFE module. In 1992, the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was employed to acquire the Indian Pines (IP) dataset, with a GSD of 20 sensors. The data collection took place over Northwestern Indiana, USA. A 10 m spectral resolution of the hyperspectral (HS) images, with 145 \(\times\) 145 pixels, encompasses 220 spectral bands, extending from 400-2500 nm. After eliminating 20 bands that are affected by noise and water absorption, the dataset still contains 200 spectral bands, particularly bands 1-103, 109-149, and 164-219. Within this scene, 16 primary land cover categories are examined. Table 2 should be consulted to ascertain the class names and the number of samples allocated to training and testing in the classification task. The spatial distribution of training and testing sets is also detailed in the work by [17]. In November 2010, the University of Southern Mississippi Gulf Park (MUUFL) in Long Beach, MS, USA, was the site of the acquisition of data using the Reflective Optics System Imaging Spectrometer (ROSIS) sensor. The dataset consists of 325 \(\times\) 220 pixels and contains 72 spectral bands. Additionally, LiDAR data are avail \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class No.** & **Class Name** & **Training** & **Testing** \\ \hline
1 & Healthy Grass & 198 & 1053 \\
2 & Stressed Grass & 190 & 1064 \\
3 & Synthetic Grass & 192 & 505 \\
4 & Tree & 188 & 1056 \\
5 & Soil & 186 & 1056 \\
6 & Water & 182 & 143 \\
7 & Residential & 196 & 1072 \\
8 & Commercial & 191 & 1053 \\
9 & Road & 193 & 1059 \\
10 & Highway & 191 & 1036 \\
11 & Railway & 181 & 1054 \\
12 & Parking Lot 1 & 192 & 1041 \\
13 & Parking Lot 2 & 184 & 285 \\
14 & Tennis Court & 181 & 247 \\
15 & Running Track & 184 & 473 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Land cover classes of the UH dataset, with the standard training and testing sets for each class. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class No.** & **Class Name** & **Training** & **Testing** \\ \hline
1 & Corn Notill & 50 & 1384 \\
2 & Corn Mintill & 50 & 784 \\
3 & Corn & 50 & 184 \\
4 & Grass Pasture & 50 & 447 \\
5 & Grass Trees & 50 & 697 \\
6 & Hay Windrowed & 50 & 439 \\
7 & Soybean Notill & 50 & 918 \\
8 & Soybean Mintill & 50 & 2418 \\
9 & Soybean Clean & 50 & 564 \\
10 & Wheat & 50 & 162 \\
11 & Woods & 50 & 1244 \\
12 & Buildings, Grass, Trees, Drives & 50 & 330 \\
13 & Stone and Steel Towers & 50 & 45 \\
14 & Alfafla & 15 & 39 \\
15 & Grass, Pasture, Towers & 15 & 11 \\
16 & Oats & 15 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Land cover classes of the IP dataset, with the standard training and testing sets for each class. information from two rasters. The first and last eight bands were removed due to noise, resulting in a total of 64 bands.