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

learning module formed by the k-means classifier. The shape, size and 3-band colour information of each input image is synthesised into the composite image of the Fast SDF k-means classifier and, then, a corresponding correlation value is recorded which translates this information into a numerical value. Then, the knowledge learning module formed by the k-means classifier will learn the coded 3D vector of the composite image together with the Red and Blue components of the spectral histogram for each input object.

We have assessed the novel Fast SDF k-means classifier using performance metrics, and, then, compared it with the k-means classifier and the Pure SDF correlator classifier, too. The k-means classifier learns the vectors of the SAP Red (x-axis) and SAP Blue (y-axis) values of the input dataset. The Pure SDF correlator classifier uses the correlation peak value of each input image which encoded their shape, size and colour information. The precision values of the Fast SDF k-means classifier for both datasets were found to be 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. Both the Pure SDF correlator classifier and the Fast SDF k-means classifier consistently performed under the different summer and winter conditions. Though the other types of surveys, e.g. boat survey can be prone to human error, applying those performance metrics with our developed Fast SDF k-means correlator classifier could be used for quality control (QC) and quality assessment (QA) of the classifier's results over the aerial survey data.

In Section 4.1, NL-DoG SDF correlator classifier was described. NL-DoG SDF correlator in comparison with the Pure SDF correlator offers improved detectability and interclass discrimination but still keep an intraclass tolerance for a higher distortion range of the true-class object. Thus, we propose in future work to integrate a NL-DoG in our Fast SDF k-means classifier design which is expected to enhance its performance.
