# Black Ice Classification with Hyperspectral Imaging and Deep Learning
## 7 Results
### Results and Discussion
For black ice in a box, the same performance measurements are also 0.93. After the whole process, predicted images are displayed in Figure 16 for distances of 10 m, 20 m, and 30 m, respectively. This clearly shows that the model that is trained on black ice data from a distance of 10 m had very robust performance throughout all the test datasets from different distances. Figure 16 is the predicted result for the best case where we have trained our model with the hyperspectral data that were taken from a distance of 10 m, as it has the best accuracy and robustness to the unknown dataset. If we observe Figure 16, by the model trained with the dataset collected from a distance of 10 m, the prediction showed higher accuracy, and with increasing distances, the accuracy dropped. However, at the 30 m distance, the black ice was still visible to some extent, and as the distance increased, the accuracy became lower. This observation shows that our model's detection works best until the camera is at a distance of 30 m from the black ice. We have compared these results with an existing method solely for black ice detection with colored image classification using a Convolutional Neural Network. This model has 64.20% OA, 78.90% precision, and 65.20% recall [28] with colored images. Due to the lack of research on black ice using hyperspectral imaging systems, we have compared our model to one of the existing state-of-the-art methods for hyperspectral image classification [18]. For a fair comparison, we have used the existing model with the same pre-processing method that we have used for our model. After the evaluation, as shown in Tables 2-4, it can be clearly observed that our proposed model is outperforming the existing model. We have mentioned the colored image classification method [28] as \"existing method 1\" and the fast 3 DCNN method [18] as \"existing method 2\". The proposed method of classifying black ice has an average of almost 15% higher accuracy than existing method 1 and on average 3-8% higher accuracy than existing method 2. After analyzing the difference, the main reason for method 1 could be the spectral and spatial information in the hyperspectral data cube, because the image cube has several dimensions of features that can be features per band and the band itself. However, for a normal RGB or color image, there is not much feature \begin{table}
\begin{tabular}{c c c c} \hline
**Accuracy Score** & **Our Model** & **Existing Method 1** & **Existing Method 2** \\ \hline F1-score (Weighted Average) & 0.93 & 0.72 & 0.85 \\ AA (\%) & 0.93 & 0.72 & 0.83 \\ OA (\%) & 0.93 & 0.73 & 0.82 \\ KA (\%) & 0.86 & 0.71 & 0.82 \\ \hline \end{tabular}
\end{table} 
Table 4: OA, AA, KA, and F1-score for black ice data from 30 m.  
Figure 16: The predicted ground truth of black ice (**A**) at 10 m (white is concentrated ice in box and gray is the ice spread on the road), (**B**) 20 m, and (**C**) 30 m (white is ice spread on the road and the gray is the concentrated ice in the box. information compared to the hyperspectral image. For method 2, the main reason could be the lack of captured spectral information. Because our model captures spectral and spatial information, it does not lose the important features in both parts.