## 4 Conclusions  
This article presented a comparative evaluation of state-of-the-art CNN-based object detection models for determining airplanes from satellite images. The networks were trained with the DOTA dataset and the performance of them was evaluated with both the DOTA dataset and independent Pleiades satellite images. The best results were obtained with the Faster R-CNN network according to the COCO metrics and F1 scores. The Yolo-v3 architecture also provided promising results with a lower processing time, but SSD could not converge the training data well with low iterations. All of the networks tended to learn more with different parameters and more iterations. It can be asserted that Yolo-v3 has a faster convergence capability when compared with the other networks; however, the optimization methods also play an important role in the process. Although SSD provided the worst detection performance, it was better in object localization. The imbalance between the object sizes and the diversities also affected the results. In the training of deep learning architectures, imbalances should be avoided, or the categories should be divided into finer grains, such as airplanes, gliders, small planes, jet planes, and warplanes. In summary, transfer learning and parameter tuning approaches on pre-trained object detection networks provided promising results for airplane detection from satellite images. Besides, the proposed slide and detect and non-maximum suppression-based detection flow enabled algorithms to be run on full-sized (large-scale) satellite images. Figure 12: Detection results of some of the DOTA test set patches (**a**) Yolo-v3, (**b**) SSD, (**c**) Faster R-CNN. For future work, the anchor box sizes can be defined by weighted clustering according to the sample size of the datasets. Moreover, all of the networks can be used together to define the offsets of the bounding boxes by averaging the predicted bounding boxes, to prevent false positives and increase the recall ratio. In this way, the localization errors could be decreased as well. Finding a way to use the ensemble learning methods for object detection architectures could be another improvement. In addition, the object detection networks often use R, G, and B bands, as they are mostly developed for natural images. However, satellite imageries can contain more spectral bands. Therefore, further studies are planned to integrate the additional spectral bands of the satellite images, to increase the number of labels and train the model more accurately. **Author Contributions:** Formal analysis, M.S. ; Methodology, U.A., M.S. and E.S. ; Project administration, E.S. ; Software, M.S. ; Validation, U.A. and E.S. ; Visualization, U.A. and M.S. ; Writing--original draft, U.A., M.S. and E.S. All authors have read and agree to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** Authors acknowledge the support of Istanbul Technical University--Center for Satellite Communications and Remote Sensing (ITU-CSCRS) by providing the Pleiades satellite images. **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A**  
```
1:procedureNMS
2:input:
3:\(d=\{b_{d}=\left[l_{x},\ l_{y},\ l_{w},\ l_{h}\right],\ c_{d}\}\): bounding box offsets
4: and confidence scores of detection list,
5:\(t\): iou threshold,
6:\(t_{s}\): score threshold. 7:output:
8:\(f_{d}=\{b_{f}=\left[l_{x},\ l_{y},\ l_{w},\ l_{h}\right],\ c_{f}\}\): final detection list.