## Data Processing
### Data Augmentation  
Our system is operating in natural environment and therefore prevailing weather has significant influence on the tonality of the images taken at the test site. Obviously, the lighting will be different in a different time of a day and a different time of a year, and thus the toning of the images will be changing according to lighting. Color temperature is a property of a light source. It is the temperature of the ideal black-body radiator that radiates light of the same color as the corresponding light source. In this context black-body radiation is the thermal electromagnetic radiation emitted by a black body. A black-body is an opaque and non-reflective body. It has a specific spectrum and intensity that depends only on the temperature of the black-body, and it is assumed to be uniform and constant. In our case, the light source is the sun that closely approximates a black-body radiator. Even though the color of the sun may appear different depending on its position, the changing of color is mainly due to the scattering of light and it is not because of the changes in the black-body radiation [13; 14; 15; 16]. Color matching functions (CMFs) provide the absolute energy values of three primary colors which appear the same as each spectrum color. We applied the International Commission on Illumination (_Commission internationale de l'eclairage_, CIE) 10-deg color matching functions in our data augmentation algorithm [17].  
The data augmentation is done according to the curves in Figure 1. Ref. [18] by converting an image into different color temperatures between 2000 K\({}^{\circ}\) and 15,000 K\({}^{\circ}\) with step size \(s\), where \(s\)\(\epsilon\) [50; 75; 100; 150; 200; 250; 300; 1000]. This makes the training set significantly larger, e.g., if \(s\) is 50, a class containing 1164 training examples becomes a class of 261 \(\times\) 1164 = 303,804 examples + the original image. The augmented data set size as a result of various value of \(s\) is given in Table 1 for the original data set of size 8 \(\times\) 1164 = 9312. After color conversion, the images are rotated by a random angle between \(-\)20\({}^{\circ}\) and 20\({}^{\circ}\) drawn from the uniform distribution. This value has been altered from 30 to 20 since our first publication because it was empirically noticed that the target birds had never a position angled this steep. Motivation for image rotation is CNN's property of being invariant to small translations but not rotation of an image [19].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Step, \(s\)** & **Number of Images for One Class** & **Number of Images for 8 Classes** \\ \hline
1100 & 15,132 & 121,056 \\
700 & 23,280 & 186,240 \\
350 & 45,396 & 363,168 \\
200 & 77,988 & 623,904 \\
100 & 153,648 & 1,229,184 \\
50 & 304,968 & 2,439,744 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of images for augmented data set with various step, \(s\), values.  
Examples of one original image and two images as an output of the augmentation algorithm with this original image as an input and \(s\) = 200 are presented in Figure 2. The color temperature of the original image is 7600 K\({}^{\circ}\) and the two augmented images 5600 K\({}^{\circ}\) and 9600 K\({}^{\circ}\), respectively.