*6.1.2 Summer dataset results*

**Figure 8** shows the K-means Clustering algorithm classification scatter plot. The classified endangered species 1 and species 2 are drawn against their SAP Red (x-axis) and SAP Blue (y-axis) values. All the objects snags were classified using their histogram spectral values of SAP Red and SAP Blue. There is a high deviation and population ratio reverse between the circa ratio of species 1 and species 2 given by the boat survey i.e. *(species1/species2) = 15:2* and the classified by the algorithm endangered species i.e. classified *species2 = 151* and classified *species1 = 96*.

**Figure 9** shows the Pure SDF Correlator classification scatter plot. The classified endangered species 1 and species 2 are drawn against their SAP Red (x-axis) and SAP Blue (y-axis) values. However, now all the objects snags were classified using their shape, size and 3-band colour information. This time the classified by the Pure SDF Correlator endangered species produced a ratio of *(species1/species2) = 11:1*.

**Figure 10** shows the Fast SDF K-means Classifier scatter plot. The classified endangered species 1 and species 2 are drawn against their SAP Red (x-axis) and SAP

#### **Figure 7.**

*Winter dataset speciation: Fast SDF K-means classifier. It shows the scatter plot of the classified endangered species 1 and species 2. All the objects snags were classified using their 3D vectors which have encoded their shape, size, 3 band colour and histogram spectral information. The classified endangered species are drawn against their SAP red (x-axis) and SAP blue (y-axis) values.*

*A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote… DOI: http://dx.doi.org/10.5772/intechopen.109028*

#### **Figure 8.**

*Summer dataset speciation: K-means clustering algorithm. It shows the scatter plot of the classified endangered species 1 and species 2. The classified endangered species are drawn against their SAP red (x-axis) and SAP blue (y-axis) values.*

#### **Figure 9.**

*Summer dataset speciation: Pure SDF correlator classifier. It shows the scatter plot of the classified endangered species 1 and species 2. All the objects snags were classified using the correlation peak value of each input image which encoded their shape, size and colour information. The classified endangered species are drawn against their SAP red (x-axis) and SAP blue (y-axis) values.*

Blue (y-axis) values. Now all the objects snags were classified using their 3D vectors which encode shape, size, 3-band colour, and spectral histogram information. This time the classified by the Fast SDF K-means Classifier produced a ratio of *(species1/species2) = 15:1*.

#### **Figure 10.**

*Summer dataset speciation: Fast SDF K-means classifier. It shows the scatter plot of the classified endangered species 1 and species 2. All the objects snags were classified using their 3D vectors which have encoded their shape, size, 3-band colour and histogram spectral information. The classified endangered species are drawn against their SAP red (x-axis) and SAP blue (y-axis) values.*

#### *6.1.3 Performance metrics*

Performance metrics have been used to assess the k-means classification algorithm, the Pure SDF correlator classifier, and the novel Fast SDF k-means classifier. Thus, precision metric is given by the ratio of true positives (TP) over the total number of false positives (FP) plus true positives:

$$\text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} \tag{12}$$

Recall metric is computed as the ratio of the TP versus the TP plus the false negatives (FN):

$$\text{Recall} = \frac{\text{TP}}{\text{TP} + \text{FN}} \tag{13}$$

True negative rate (TNR) is computed as the ratio of the true negatives (TN) versus the TN plus the FP:

$$\text{TNR} = \frac{\text{TN}}{\text{TN} + \text{FP}} \tag{14}$$

Accuracy is given by the ratio of TP plus TN over the sum of TP, TN, FP and FN:

$$\text{Accuracy} = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}} \tag{15}$$

**Table 1** shows the performance metric values for the winter dataset of all the three tested classifiers. In effect, the second column shows the performance metric values


#### **Table 1.**

*Winter dataset speciation performance metric values for the k-means clustering algorithm, the pure SDF correlator and the fast SDF k-means classifier.*


#### **Table 2.**

*Summer dataset speciation performance metric values for the k-means lustering algorithm, the pure SDF correlator and the fast SDF k-means classifier.*

of the k-means classification algorithm, the third column shows the performance metric values of the Pure SDF correlator classifier, and the fourth column shows the performance metric values of the novel Fast SDF k-means classifier. Similarly, **Table 2** shows the performance metric values for the summer dataset of all the three evaluated classifiers. The results are shown on the corresponding columns of **Table 2** as for **Table 1**.
