## 1 Introduction
In addition, unlike the natural images, some of the objects that should be detected in satellite images are represented with few numbers of pixels due to the limitations of the sensor's spatial resolution. Moreover, the presence of a multi-perspective data set is important to obtain highly accurate detection results. Although it is not a difficult task to create multi-perspective data sets using natural images, this could be challenging with satellite sensors. This challenge could be partially overcome by using satellite images obtained with different incidence angles to account for perspective differences in the training phase. Lastly, the atmospheric conditions and sun angle should be considered for satellite images as they affect the spectral response of the objects. [PERSON] et al. proposed the creation of a large-sized dataset named NWPU-RESISC45 to overcome the lack of training samples that are derived from satellite images. Their network consists of 31,500 image chips related to 45 land classes and they reported an obvious improvement in scene classification by implementing this dataset on pre-trained networks [22]. [PERSON] et al. worked on the detection of aircraft from unmanned aerial vehicle (UAV) imageries with YOLO and achieved a 99.6% precision rate [39]. [PERSON] et al. used SSD to detect the various sizes of ships inshore and offshore areas using a transfer-learned SSD model with 87.9% average precision and outperformed the Faster R-CNN model, which provided an 81.2% average precision [40]. [PERSON] et al. tried two sizes of detectors (SSD300 and SSD512) with SAR images for the same purpose and achieved 92.11% and 91.89% precisions, respectively [41]. The main objective of this research was to develop a framework with a comparative evaluation of the state-of-the-art CNN-based object detection models, which are Faster R-CNN, SSD, and YOLO, to increase the speed and accuracy of the detection of aircraft objects. To increase the detection accuracy, VHR satellite images obtained from different incidence angles and atmospheric conditions were introduced into the evaluation. As mentioned above, the trained data availability for the satellite images is limited, which is an important drawback in CNN-based architectures. Thus, this research proposes the use of a pre-trained network as a base, and improves the training data by comparatively less number of samples obtained from satellite images. In addition, default bounding boxes are generated with six different aspect ratios at every feature map layer to detect objects more accurately and faster. The detection models were trained with a labeled dataset produced from satellite images with different acquisition characteristics and by the use of the transfer learning approach. The training processes were performed repeatedly with different optimization methods and hyper-parameters. Although the accuracy is very important, it must be taken into account that the framework needs to process very large-scale satellite images quickly. Thus, a detection flow was developed to use trained models in the simultaneous detection of multiple objects from satellite images with large coverage. This research aimed to significantly contribute to the CNN-based object detection field by:  
- Improving the performance of state-of-the-art object detectors on the satellite image domain by improving the learning with a patched and augmented \"A Large-scale Dataset for Object DeTection in Aerial Images (DOTA)\" satellite dataset (transfer learning) and hyperparameter tuning. - Providing a detection flow that includes the slide-and-detect approach and non-Maximum suppression algorithm, to enable fast and accurate detection on large-scale satellite images. - Providing a comparative evaluation of object detection models across different object sizes and different IOUs and preform an independent evaluation with full-sized (large-scale) Pleiades satellite images that have different resolution specs than the training dataset to investigate the transferability.