## 4 Experimental Results  
### Dataset Description  
To evaluate the classification performance of the HDCDF network, we used three representative HSI datasets in our experiments: Indian Pines (IP), University of Pavia (UP), and Salinas Valley (SV). The false-color composite, ground-truth maps, and details of each category are shown in Figures 11-13. Further details regarding these datasets are presented in the following. Figure 11: IP dataset. (**a**) False-color composite; (**b**) ground-truth map; (**c**) land cover categories in the IP dataset. Figure 12: UP dataset. (**a**) False-color composite; (**b**) ground-truth map; (**c**) land cover categories in the UP dataset. Indian Pines (IP) dataset: The hyperspectral image was acquired by the AVIRIS sensor (manufactured by NASA's Jet Propulsion Laboratory (JPL) in Pasadena, CA, USA) over the Indian Pines test site in north-western Indiana in 1992, consisting of 16 land cover classes, mainly including corn, soybean, and oats. This dataset consists of 145 \(\times\) 145 pixels and 220 spectral bands in the wavelength scope of 0.4-2.5 \(\upmu\)m. We removed 20 water absorption and low-signal-to-noise-ratio bands and kept the remaining 200 bands for our experiments. University of Pavia (UP) dataset: The UP dataset was captured by the ROSIS-03 sensor (developed by the German Aerospace Center (DLR) in Oberpfaffenhofen, Germany) in Northern Italy in 2001, with a size of 610 \(\times\) 340 and a high spatial resolution of 1.3 m per pixel. A total of 103 spectral bands were obtained after removing some low-signal-to-noise-ratio bands in the spectral coverage ranging from 0.43 to 0.86 \(\upmu\)m. In addition, the existing ground truths are divided into nine classes, such as meadows, trees, bitumen, and so on. Salinas Valley (SV) dataset: Collected by the AVIRIS sensor in 1998 over the Salinas Valley in California, this dataset features a high resolution of 3.7 m per pixel with a size of 512 \(\times\) 217. Since 20 of the bands cannot be reflected by water, we selected the remaining 204 bands with a wavelength scope of 0.4 \(\upmu\)m to 2.5 \(\upmu\)m to conduct the experiments. The available ground truth for the SV contains 16 categories. Deep learning structures are data-driven, meaning they need numerous labeled training samples to fully train a network for perfect classification results, but more data mean higher computation complexity and more time consumption. Therefore, we performed experiments to verify that the proposed HDCDF can maintain excellent classification performance in the case of very limited training samples. In Tables 1-3, we can see the details of each dataset used in our experiments. During the experiment, we set the size of the training sample and the validation sample at the minimum level. Since each class of UP and SV samples is more sufficient than IP, only 0.5% training samples and 0.5% validation samples are selected, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline
**No** & **Class** & **Total** & **Training** & **Val** & **Test** \\ \hline
1 & Alfalfa & 46 & 2 & 2 & 42 \\
2 & Corn—no till & 1428 & 71 & 71 & 1286 \\
3 & Corn—min till & 830 & 42 & 42 & 746 \\ \hline \end{tabular}
\end{table} 
Table 1: The details of the Indian Pines (IP) dataset with 5% training samples. Figure 13: SV dataset. (**a**) False-color composite; (**b**) ground-truth map; (**c**) land cover categories in the SV dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline
**No** & **Class** & **Total** & **Training** & **Val** & **Test** \\ \hline
1 & Asphalt & 6631 & 33 & 33 & 6565 \\
2 & Meadows & 18,649 & 93 & 93 & 18,463 \\
3 & Gravel & 2099 & 10 & 10 & 2079 \\
4 & Trees & 3064 & 15 & 15 & 3034 \\
5 & Painted metal sheets & 1345 & 7 & 7 & 1331 \\
6 & Bare Soil & 5029 & 25 & 25 & 4979 \\
7 & Bitumen & 1330 & 7 & 7 & 1316 \\
8 & Self-Blocking Bricks & 3682 & 18 & 18 & 3646 \\
9 & Shadows & 947 & 5 & 5 & 937 \\ \hline  & Total & 42,776 & 213 & 213 & 42,350 \\ \hline \end{tabular}
\end{table}