## 1 Introduction  
Object detection from satellite imagery has considerable importance in areas, such as defense and military applications, urban studies, airport surveillance, vessel traffic monitoring, and transportation infrastructure determination. Remote sensing images obtained from satellite sensors are much complex than computer vision images since these images are obtained from high altitudes, including interference from the atmosphere, viewpoint variation, background clutter, and illumination differences [1]. Moreover, satellite images cover larger areas (at least 10 kmx10 km for one image frame) and represent the complex landscape of the Earth's surface (different land categories) with two-dimensional images with less spatial details compared to digital photographs obtained from cameras. As a result, the data size and areal coverage of satellite images are also bigger compared to natural images. In object detection studies with satellite imagery, the visual interpretation approach that benefits from experts' knowledge for the identification of different objects/targets is still widely used. The accuracy of this approach is dependent on the level of expertise and the approach is time consuming due to the manual process [2]. Several studies have been conducted on the automatic identification of different targets, such as buildings, aircraft, ships, etc., to reduce human-induced errors and save time and effort [1; 3; 4]. However, the complexity of the background; differences in data acquisition geometry, topography, and illumination conditions; and the diversity of objects make automatic detection challenging for satellite images. The object detection task can be considered as a combination of two fundamental tasks, which are the classification of the objects and determination of their location on the images. Studies conducted so far have focused on improving these two tasks separately or together [1; 5]. In the early studies, the majority of the studies were conducted with unsupervised methods using different attributes. For example, the scale-invariant feature transform (SIFT) key points and the graph theorem were used for building detection from panchromatic images [6]. Alternatively, a wavelet transform was utilized in ship detection from synthetic aperture radar (SAR) images [7]. However, such unsupervised methods generally provided efficient results for simple structure types, and the results were successful for a limited variety of objects. Later studies focused on supervised learning methods so that objects with different constructions could be identified with high performance from more complex scenes [8; 9]. The main reason behind the more successful results with supervised learning is that the learning process during the training phase is performed with previously manually labeled samples. Before the use of convolutional neural network (CNN) structures became widespread, different supervised learning methods were utilized with handcrafted features. In previous research, a spatial sparse coding bag-of-words (BOW) model was developed for aircraft recognition through the SVM classifier and the results were better than the traditional BOW model [10]. Gabor filters with SVM were used to detect aircraft, and achieved a 91% detection rate (DR) with a 7.5% false alarm rate (FAR) [11]. A deformation model representing the relation of the roots and parts of the objects by utilizing an extracted histogram of oriented gradient (HOG) features at different scales of images was developed and trained in a discriminatory manner as a framework for object recognition using a mixed model [12]. In another study, a probabilistic latent semantic analysis model (pLSA) and a K-Nearest Neighbor (k-NN) classifier with bag-of-visual-words (BoVW) was used for landslide detection [13]. In a more recent research, visually saliency and sparse coding methods were combined to efficiently and simultaneously recognize the multi-layered targets from optical satellite images [14].