## 2 Data and Methods
### Data and Augmentation  
The DOTA dataset was used for training and testing purposes. It is an open-source dataset for object detection purposes from remote sensing images. The dataset includes satellite image patches obtained from the Google Earth(r) platform, and Jilin 1 (JL-1) and Gaofen 2 (GF-2) satellites. It contains 15 object categories as airplane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field, and swimming pool. The image sizes are in the range of \(800\times 800\) to \(4000\times 4000\). In this study, airplane detection was aimed for; therefore, 1631 images that contained 5209 commercial airplane objects were selected from the dataset. The images were split to the size of \(1024\times 1024\) patches to train Faster R-CNN and \(608\times 608\) for training SSD and YOLO-v3 detectors. The spatial resolution of the images varies in range 0.11 to 2 m and they contain various orientations, aspect ratios, and pixel sizes of the objects. In addition, the images vary according to the altitude, nadir-angles of the satellites, and the illumination conditions. The selected images were separated as 90% for training and the rest for testing. The DOTA training and test sets also include different samples in terms of airplane dimensions, background complexity, and illuminance conditions. Some image patches have some cropped objects, and some examples are black and white panchromatic images. These variations in the DOTA dataset enable the trained object detection architectures to achieve a similar performance in different image conditions (Figure 1).  
Figure 1: Patches from the DOTA test set; (**a**) cropped, (**b**) very big, (**c**) very small, (**d**) complex background, (**e**) illuminance effect, and (**f**) panchromatic samples.  
Moreover, independent testing was performed with five image scenes obtained from very high-resolution pan-sharpened Pleiades 1A&1B satellite images with a 0.5-m spatial resolution and four spectral channels. In this research, Red/Green/Blue (RGB) bands of the Pleiades images were used. Satellite images were acquired in different atmospheric conditions but mostly at cloudless days and at different times in daylight. Images were collected between 2015 and 2017 in different seasons except for the winter. The images contain the Istanbul Ataturk, Istanbul Sabiha Gokeen, Izmir Adnan Menderes, Ankara Esenboga, and Antalya airport districts. They cover about a 53 km\({}^{2}\) area and contain 280 commercial airplanes. Properties of the Pleiades VHR images are provided in Table 1.  
When the bounding box area distributions of aircraft samples were investigated for the DOTA training, DOTA test, and Pleiades image datasets, it was revealed that the DOTA train set includes almost the same distribution as the DOTA test set, with areas between 0 and 15,000 pixels, while it differs slightly from the samples in the large-scale Pleiades image data set. There is no object sample over 20,000 pixels in the large-scale test set and the areas of the samples are mostly between 3000 and 6000 pixels (Figure 2).  
In deep architectures, a large number of labeled data is significant. Thus, the data augmentation has vital importance to cope with a lack of labeled data and to have robustness in the training step. Horizontal rotation and random cropping were applied as augmentation techniques. Besides, the image chips were scaled in HSV (hue-saturation-value) to imitate atmospheric and lighting conditions (Figure 3).