# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
## 4 Results and Discussion  
Here, we contrast the classification results achieved using the suggested strategy based on Capsule-Net with those acquired using alternative deep learning-based techniques, such as DSVM, DCNN, CNN, R-VCA, and 3 DCNN. In order to enhance the HSI categorization, DSVM attempts to employ a number of filters in the deep SVM classifier, including the logarithmic radial base function (rbf) and the normal distribution rbf. The CNN model seeks to carry out the HSI categorization by taking into account both spectral information and geographical context. Triplet loss is used by DCNN to enhance HSI categorization. R-VCA uses a vertex curve fitting network with rolling guiding filter to include geographical data and spectral features into the classification process. The spatial and spectral information are taken into consideration concurrently by CNN using a 3D convolution layer.  
The achieved performance by different classes of the Indian Pines HSI are reported in Table 1. These findings demonstrate that the suggested network, based on Capsule-Net, outperforms alternative deep learning-based approaches in terms of classification accuracies. Table 2 shows the performance for the SV, UP, and IP datasets. It is feasible to see that the proposed approach offers the best overall results as well as the highest accuracy values. The achieved results show that the suggested method offers a notable quantitative improvement, showing that the suggested spectral-spatial framework can produce more unique characteristics to successfully classify remotely sensed HSIs, achieving the highest detection accuracy for all the tests carried out.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Models** & & & & & \\ \hline  & DSVM & DCNN & CNN & R-VCA & 3 DCNN & Proposed \\ C1 & 88.22 & 94.21 & 93.41 & **95.02** & 94.46 & 84.2 \\ C2 & 92.31 & 95.41 & 96.80 & 94.91 & 92.19 & **98.6** \\ C3 & 95.12 & 96.98 & 95.26 & 97.36 & 98.05 & **100** \\ C4 & 95.96 & 97.56 & 93.82 & 96.99 & 97.09 & **98.9** \\ C5 & 97.03 & 95.11 & 96.22 & 94.78 & 92.16 & **100** \\ C6 & 83.03 & 96.51 & 94.02 & 98.89 & 95.10 & **100** \\ C7 & 89.64 & 95.63 & 95.04 & 96.14 & 94.06 & **100** \\ C8 & 92.03 & 97.10 & 98.01 & 95.31 & 98.91 & **100** \\ C9 & 96.12 & 94.12 & 97.02 & 93.36 & **97.03** & 87.5 \\ C10 & 91.22 & 91.85 & 98.04 & 94.21 & 94.02 & **99.2** \\ C11 & 85.13 & 93.45 & 95.32 & 94.21 & 95.21 & **99.6** \\ C12 & 91.12 & 89.48 & 86.14 & 92.14 & 95.03 & **100** \\ C13 & 86.12 & 94.25 & 94.06 & 93.84 & 96.21 & **100** \\ C14 & 95.01 & 93.85 & 97.13 & 94.96 & 96.12 & **99.4** \\ C15 & 95.02 & 94.61 & 95.23 & 95.95 & 94.18 & **99.4** \\ C16 & 92.38 & 96.85 & 96.10 & 95.91 & 95.19 & **100** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance by different classes of the Indian Pines HSI.  
The suggested approach may incorporate more 3D volume locations as the network depth rises thanks to the structure of the network, which keeps increasing the feature map dimension by including the Caps-Net when compared with normal CNN. This fact ultimately encourages the discovery of a wider range of high-level spectral-spatial features, evenly distributing the workload between many units to speed up network training and also enabling the model to lessen the weakening phenomenon when taking significantly deep networks into account. We can draw the conclusion that the suggested methodology is more able to exploit the spectral-spatial data present in an HSI data cube and maintain strong quantitative performance with tiny kernel spatial sizes based on the published results with distinct HSI datasets.