# GPR Data Interpretation Approaches in Archaeological Prospection
## 6 CNN Application to GPR Data
### Prediction Results  
The prediction results are summarized in the confusion matrices (Figure 4). The results indicated that Model B performs overall better than Model A, reaching a classification score of 92% over 85%. This shows that the random splitting approach should be preferred as it is easier to train and generalizes better. However, Model A was better than Model B in the predictions made for the anomaly class. More specifically, Model A was 100% accurate having predicted all 32 examples correctly, while Model B predicted 30 out of 32 examples correctly (Anomaly class in Figure 4). On the other hand, Model B is more accurate for the other two classes, predicting correctly 29 out of 32 noise examples. Model B predicted 25 and 33 out of 36 structure examples, whereas Model A, 28.  
All of the misclassified examples are presented in Figure 5. The incorrect predictions made by Model B for the Anomaly class are examples #21 and #23 that were classified as structures. These examples are different views of the same structure resulting from the overlapping sliding window approach. In addition, example #23 is a marginally correct prediction by Model A (52.7% as an anomaly and 47.3% as a structure). Further, example #24, which is another view of the same feature, is correctly classified by both models. This  
Figure 3: The evaluation set used to test the final models’ generalization.  
As for the noise class, Model A made seven mistakes, while Model B made three. Example #38 was classified as a structure by Model B, and more likely, was mistaken as a wall. Similarly, examples #39 and #40 were mistaken as structures by both models, with example #39 being a marginally negative prediction for Model A (43.3% as noise, 56.7% as structure). However, Model B performed better in identifying correctly noise derived from the surface such as plowing lines, as presented in examples #42, #43, #58, and #59. Example #57 has three different noise types appearing in three different orientations: vertical, horizontal, and diagonal. Model B prediction was accurate, while Model A gave mixed results, having classified 53.3% as an anomaly, 28.6% as noise, and 18.1% as a structure.  
The structure class incorrect predictions for Model A are #82 to #87, #89, and #95, which were classified as an anomaly instead of structure. Examples #82 to #85 are different views of the same structural feature, which bears a unique pattern probably due to a collapsed roof. Model B was also mistaken (examples #82 and #84) but it managed to predict examples #83 and #85 correctly, with the latter being a marginal case. Example #85 shows this particular not well-preserved structure at a different depth. The rest of the examples were derived from a structural complex, and Model B was overall more accurate than Model A.  
The obtained results indicate that Model B generalizes better than Model A suggesting that the random approach to split the test set and training set is preferable in this case scenario where available data were not enough to provide enough and unique examples from different case studies. However, the results imply that further data are required in the training datasets to improve the prediction robustness.