# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 5 Results
### Feature Extraction Results  
Upon completing the training, we extracted the terrain features before and after excavation and input them into our GridNet. The visualized results are shown in Figure 7.  
Figure 5: Real-time decision-making process.  
Figure 6: Excavation result based on EGTN.  
A comparison between the feature maps before and after training reveals a noticeable difference: post-training, there is an increased level of feature activation, indicating that the network's ability to extract features has improved.  
The images on the left represent the terrain before excavation, while the images on the right depict the terrain after excavation. In both cases, the feature maps are shown for pre-training and optimal-training weights. For the first layer feature maps, the pre-training images are more chaotic and less defined, while the optimal-training images show more distinct patterns and clearer feature representations. This suggests that the network has learned to emphasize relevant features for the task at hand.  
Similarly, the last layer feature maps exhibit a stark contrast. The pre-training images are almost uniform with little to no distinctive features, whereas the optimal-training images display well-defined, high-activation regions. This signifies that the network, after training, can better identify and highlight the most critical features of the terrain.  
The scale on the right indicates the level of activation, with higher values corresponding to stronger activations. The optimal-training feature maps generally show higher activation levels compared to the pre-training ones, which is a strong indicator of the network's enhanced feature extraction capability after training.  
In our study, we employed principal component analysis (PCA) as a preliminary method to assess the effectiveness of feature extraction by GridNet. Figure 8b presents the PCA results of the feature vectors extracted by GridNet. The first two principal components account for an explained variance ratio of 0.39 and 0.16, respectively, cumulatively explaining over 50% of the variance. The scatter plot associated with these components shows a relative dispersion of data points, lacking a clear clustering trend. This dispersion suggests that the extracted features have a high level of complexity and may capture more nuanced variations within the data.  
Conversely, Figure 8a illustrates the PCA results of the raw grid data. Here, the first two principal components have an explained variance ratio of 0.25 and 0.19, which together explain less than 50% of the total variance. The scatter plot reveals a concentrated distribution of data points, indicating a strong clustering tendency. This concentration implies that the raw data may inherently contain less complexity or fewer distinct patterns before the application of feature extraction techniques.  
The comparative analysis of the PCA results before and after the application of GridNet allows us to infer that the feature extraction process not only accounts for a greater proportion of the variance but also increases the complexity of the feature representation. This is indicated by the spread of data points in the scatter plot post-extraction, which contrasts with the more clustered distribution seen in the raw data. The ability of GridNet  
Figure 7: Comparison of feature map activations.  
to elucidate a higher amount of variance and to disperse the data points suggests that it is capturing more significant variables within the dataset.  
The feature extraction process through GridNet, as evidenced by the PCA, appears to be effective in distilling important variables from the data. This is an encouraging sign that the network can isolate and amplify aspects of the data that are most informative for subsequent analysis. The increased complexity and variance explained by the extracted features are indicative of GridNet's robustness in representing terrain data in a way that is conducive to advanced terrain analysis and decision-making processes in automated systems.