**6. Results**

In this section, the datasets used will be described. Then, the details of the recorded results will be shown together with the Fast SDF K-means performance metrics for the different datasets.

#### **6.1 Datasets**

Two different datasets have been used for testing the performance of the Fast SDF K-means classifier: (a) Winter Dataset which consisted of aeriial images taken during the Winter months, and (b) Summer Dataset which consisted of aerial images taken during the Summer months. Winter Dataset consisted of 221 three-band JPEG formatted aerial image shots also known as snags. Each snag had the size of [320240] pixels. Summer Dataset consisted of 270 three-band JPEG formatted snags. Each snag had, as for the Winter Dataset, the size of [320240] pixels. For aerial survey logging and identification reasons, all the shapefiles ".jgw" and ".mat" tagged information files have been saved for both datasets, too. To match the aerial survey automated image analysis of the snags with the ground truth data an object identification (ObjectID) information had been provided with each snag. Ground truth data had been collected from sea surveys or on-shore remote view including the total number of endangered species 1 and the total number of endangered species 2.

#### *6.1.1 Winter dataset results*

**Figure 5** 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

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

#### **Figure 5.**

*Winter 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. There is a high number of endangered species which their ID tagged by the GT scientists was uncertain i.e. tagged as* "species 1 or species 2".

survey i.e. *(species1/species2) = 13:1* and the classified by the algorithm endangered species i.e. classified *species2 = 185* and classified *species1 = 36*.

**Figure 6** 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) = 24:2*.

#### **Figure 6.**

*Winter 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. There is a high number of endangered species which their ID tagged by the GT scientists was uncertain i.e. shown on the plot* as "species 1 or species 2".

**Figure 7** 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 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) = 20:4*.
