## 4 Experimental Results
### Classification Maps and Categorized Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No** & **Class** & **3 DCNN** & **DBMA** & **SpectralFormer** & **ExViT** & **LSSMA** & **Proposed** \\ \hline
1 & Alfalfa & 83.93 & 95.75 & 93.41 & 97.64 & 94.99 & **100** \\
2 & Corn\(-\)no till & 87.68 & **96.62** & 93.37 & 78.12 & 93.74 & 95.06 \\
3 & Corn\(-\)min till & 89.04 & 94.26 & 90.25 & 94.72 & 83.52 & **97.72** \\
4 & Corn & 82.33 & 92.98 & 89.04 & 90.50 & 91.66 & **95.36** \\
5 & Grass\(-\)pasture & 82.36 & 93.65 & **99.59** & 97.59 & 68.74 & 95.16 \\
6 & Grass\(-\)trees & 96.42 & 97.69 & 81.97 & 89.83 & 89.47 & **100** \\
7 & Grass\(-\)pasture (moved) & 84.28 & 66.84 & 99.34 & 98.54 & 95.76 & **100** \\
8 & Hay\(-\)windowed & 89.62 & 94.69 & 64.28 & 83.51 & 80.14 & **99.81** \\
9 & Oats & 78.85 & 89.57 & 89.02 & **93.61** & 91.75 & 91.89 \\
10 & Soybean\(-\)no till & 91.55 & 88.36 & 90.93 & **98.38** & 96.33 & 93.22 \\
11 & Soybean\(-\)min till & 92.59 & 98.75 & 93.69 & 96.48 & 93.97 & **100** \\
12 & Soybean\(-\)clean & 88.78 & 83.19 & 98.62 & **100** & 99.21 & 90.45 \\
13 & Wheat & 84.02 & 95.65 & 95.34 & 98.04 & 84.74 & **98.20** \\
14 & Woods & 93.61 & 95.57 & 90.47 & 87.74 & 85.52 & **98.82** \\
15 & Building/grass/trees/drivers & 84.47 & 93.45 & 91.27 & 93.39 & **94.42** & 90.85 \\
16 & Stone/Steel/Towers & 93.55 & 92.27 & 90.12 & 91.86 & 87.15 & **99.75** \\ \hline OA (\%) & & 89.95 & 92.55 & 91.13 & 94.59 & 94.28 & **95.73** \\ AA (\%) & & 87.69 & 91.83 & 90.57 & 93.27 & 89.49 & **96.51** \\ Kappa (\%) & & 88.04 & 91.48 & 90.81 & 93.04 & 92.89 & **95.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results of the IP dataset with 5% training samples and the best accuracy each class is in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No** & **Class** & **3 DCNN** & **DBMA** & **SpectralFormer** & **ExViT** & **LSSMA** & **Proposed** \\ \hline
1 & Asphalt & 90.24 & 95.48 & 95.75 & **98.24** & 97.88 & 97.27 \\
2 & Meadows & 96.48 & **98.99** & 98.92 & 98.86 & 98.16 & 98.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results of the UP dataset with 0.5% training samples and the best accuracy each class is in bold. At the same time, from the classification diagram, we can see that the network proposed in this paper handles the boundary well. Taking class 16, located in the lower left corner of Figure 17, which only has nine training samples in the SV dataset, as an example, the method proposed performs the best among these models and finally obtains a remarkable result of 100%, while the performance of other networks is not accurate, especially LSSMA. It can be seen from Figure 17 that except for HDCDF and LSSMA with poor recognition effects, there are a few cases of incorrect classification on the boundary of other models. In general, the accuracy of the proposed HDCDF network on most of the three datasets exceeds 93% for each class; it has strong feature extraction capability and is superior to other models in boundary processing. The computational efficiency of the HDCDF model is apparent in terms of parameter training and testing times on the IP dataset using 5% training samples in Table 7. A network with good performance should balance accuracy and efficiency properly. The above experiments have proven that our proposed method can obtain excellent performance with limited data. From Table 7, on account of 3 DCNN containing more parameters (1481.6 K) to be trained, other methods take less time than 3 DCNN. HDCDF takes less time and memory compared to models based on transformer. Although LSSMA possesses fewer parameters  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No** & **Class** & **3 DCNN** & **DBMA** & **SpectralFormer** & **ExViT** & **LSSMA** & **Proposed** \\ \hline
1 & \(\text{Brocoli\_green\_weeds\_1}\) & 99.87 & 99.87 & 99.91 & **100** & 99.98 & **100** \\
2 & \(\text{Brocoli\_green\_weeds\_2}\) & 98.9 & 99.47 & **100** & **100** & 97.81  
(8.7 K) than the proposed model (22.5 K), HDCDF has a shorter training time and a higher accuracy of nearly 1.5%.