**7. Discussion and conclusions**

In this section, the results of our evaluated classifiers are analysed and compared. In particular, the discussion is focused on the performance of the three classifiers during the winter dataset tests since the endangered *species 1* and *species 2* offer a greater classification challenge during the winter months than the summer ones. A separate section is included with the main conclusions of this work together with some future research suggestions.

## **7.1 Discussion**

From **Figures 5**–**7** it can be found that, overall, the Fast SDF k-means has performed better than the k-mean classification algorithm and the Pure SDF correlator classifier, too, for the winter dataset. Though the Pure SDF correlator classifier gave a classification rate of *(species1/species2)* closer to the ground-truth (GT) scientists ratio than the Fast SDF k-means, it still had a much higher, almost the double of the total number of classified endangered species i.e. sum of species 1 and species 2, of uncertain classifications which cannot be matched with either species1 or species2. Similarly, from **Figures 8**–**10** it can be found that, overall, the Fast SDF k-means has performed better than the k-mean classification algorithm and the Pure SDF correlator classifier, too, for the summer dataset. Thus, the classification rate of *(species1/ species2)* for the Fast SDF k-means was almost identical to the classification rate of the GT scientists. By incorporating the shape, size, 3-band colour and histogram spectral information into the Fast SDF k-means classifier it has improved the classification performance for both summer and winter datasets in comparison to the other two classifiers.

From **Tables 1** and **2** the performance of all the classifiers can be assessed which they were used for the winter endangered bird species plumage and summer endangered bird species plumage speciation. It is can be clearly shown that k-means classification algorithm performed worse than the Pure SDF Correlator and Fast SDF Kmeans classifiers for both summer and winter endangered bird species plumage. In effect, the precision value was 96.11% and the accuracy value was 89.6% for the Fast SDF k-means classifier when tested with the winter dataset. The precision value became 89.93% and the accuracy value reached 83.23% for the Fast SDF k-means classifier when tested with the summer dataset. It worth of mentioning that the precision values of the Fast SDF k-means classifier for both datasets were higher than the human image analysts values which was not more than 88% for the winter plumage and not more than 89% for the summer plumage.

It should be noted that during the summer and winter surveys the weather conditions were significantly different. In effect, during the winter aerial survey the weather conditions were poor but during the summer boat survey the conditions were significantly improved. Thus, by observing the performance metric values of **Tables 1** and **2**, it can be concluded that the Pure SDF Correlator Classifier's performance has not been significantly affected due to the different weather conditions when the surveys were conducted. Also, though the performance of Fast SDF k-means classifier seems to deviate on summer survey from the winter survey this was found to be due to glint effects in the capture image data. After examining the summer datasets, it was identified approximately 25% of the total number of snags to have significant glint effects in them. Nevertheless, the overall performance of the Pure SDF correlator classifier and the Fast SDF k-means classifier closely matched the boat survey during both winter and summer weather conditions.

Further, the novel Fast SDF k-means classifier has minimised the amount of total data needed to be ground-truthed by the GT scientists i.e. it can lead to an increased automation of the speciation process. We assessed the Fast SDF k-means classifier precision and accuracy values to be greater than 85% during the winter surveys i.e. approximately only 20% or less of the total amount of survey data would need to be ground-truthed. Hence, that would make more cost-effective the processing of the datasets i.e. more surveys per day would become possible to be processed by the GT scientists.

### **7.2 Conclusion**

We have shown how our novel cognitive architecture of the Fast SDF k-means classifier is conceptually connected with the image analysis processing cycle. It combines a hybrid digital-optical design where the k-means unsupervised learning algorithm is integrated with a correlator. Thus, Fast SDF k-means classifier consists of a knowledge representation module formed by the SDF correlator and a knowledge
