# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 4 Experimental Result and Discussion
### Experimental Results and Analysis  
To verify the effectiveness of our method, we compared it with eight advanced methods: SVM (a machine learning method) [5], HybridSN [10], SSRN [11] (deep learning methods), and five advanced few-shot learning methods--DFSL [13], DCFSL [14], GIA-CFSL [15], CDFSCL [27], and ADAFSSL [16]. Among these, DFSL employs a nearest neighbor classifier scheme. All comparative experiments were conducted with five labeled samples per class to ensure fair comparison. Tables 4-6 show the classification accuracy for each class, including OA (Overall Accuracy), AA (Average Accuracy), and Kappa coefficient, the top performance for each class is marked in bold. It is evident that our method outperforms these comparative experiments, which proves that our proposed approach is highly effective. From Tables 4-6, it can be seen that under the same settings, our proposed method has increased the OA by 4.45% and 6.49%, the AA by 6.13% and 5.46%, and the Kappa by 6.15% and 8.22% compared to GIA-CFSL and ADAFSSL, respectively, on the UP dataset. The proposed method surpasses the OA of CDFSCL and ADAFSSL by 9.01% and 6.08%, AA by 6.93% and 3.53%, and Kappa by 10.09% and 6.67% on the IP dataset. On the Salinas dataset, the OA of DIFSCL exceeded CDFSCL and ADAFSSL by 2.24% and 2.54%, AA by 0.93% and 0.97%, and Kappa by 2.47% and 2.81%, respectively. Our proposed method also performs well in classifying categories that comparative methods cannot distinguish effectively, like Asphalt, Trees, and Self-Blocking Bricks in the UP dataset, and nine categories in the IP dataset. The Grapes category, with generally low classification performance among all methods in Salinas, further proves the effectiveness of our approach. As a comparison, Figures 7-9 display the classification maps of these methods on three datasets, demonstrating that our DIFSCL method achieves superior classification performance.  
Figure 7: Classification maps obtained using different methods on the UP dataset. (**a**) Ground truth. (**b**) SVM. (**c**) HybridSN. (**d**) SSRN. (**e**) DFSL+NN. (**f**) DCFSL. (**g**) Gia-CFSL. (**h**) CDFSCL. (**i**) ADAFSSL. (**j**) DIFSCL.  
Figure 8: Classification maps obtained using different methods on the IP dataset. (**a**) Ground truth. (**b**) SVM. (**c**) HybridSN. (**d**) SSRN. (**e**) DFSL+NN. (**f**) DCFSL. (**g**) Gia-CFSL. (**h**) CDFSCL. (**i**) ADAFSL. (**j**) DIFSCL.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Class** & **SVM** & **Hybrid SN** & **SSRN** & **DFSL** & **DCFSL** & **Gia-CFSL** & **CDFSCL** & **ADAFSL** & **DIFSCL** \\ \hline
1 & 57.18 & 33.53 & 76.77 & 61.53 & 80.77 & 81.10 & 75.58 & 81.79 & **92.43** \\
2 & 60.00 & 82.85 & 72.58 & 72.89 & 85.79 & **89.11** & 88.41 & 82.66 & 88.37 \\
3 & 41.07 & 78.18 & 72.49 & **83.80** & 61.54 & 64.49 & 64.25 & 69.72 & 73.84 \\
4 & 82.50 & 65.41 & 82.21 & 87.67 & 93.54 & 91.66 & 92.38 & 86.81 & **96.52** \\
5 & 98.26 & **99.90** & 99.87 & 99.44 & 98.48 & 99.63 & 99.13 & 97.99 & 98.93 \\
6 & 53.70 & **83.46** & 77.67 & 80.83 & 76.77 & 73.20 & 74.86 & 77.30 & 77.58 \\
7 & 75.21 & **99.28** & 90.31 & 90.73 & 78.11 & 79.25 & 81.18 & 86.31 & 91.24 \\
8 & 69.27 & 65.53 & 71.74 & 62.59 & 67.44 & 73.84 & 70.06 & 74.76 & **89.90** \\
9 & **99.93** & 57.09 & 99.18 & 88.31 & 98.24 & 98.06 & 97.87 & 98.96 & 96.67 \\  & 62.86 & 72.78 & 76.43 & 74.50 & 82.17 & 83.88 & 82.67 & 81.84 & **88.33** \\ OA & \(\pm\)5.41 & \(\pm\)4.59 & \(\pm\)1.88 & \(\pm\)5.44 & \(\pm\)4.33 & \(\pm\)2.82 & \(\pm\)3.84 & \(\pm\)4.47 & **+3.65** \\ AA & 70.79 & 73.91 & 82.54 & 80.87 & 82.30 & 83.37 & 82.63 & 84.04 & **89.50** \\  & \(\pm\)2.57 & \(\pm\)3.62 & \(\pm\)4.53 & \(\pm\)2.02 & \(\pm\)2.58