Journal of Space Technology, Vol 7, No 1, July 2017 44 Figure 1.Conceptual level block diagram of the proposed target detection system Abstract— Automatic target detection in satellite images is a challenging problem due to the varying size, orientation and background of the target object. The traditionally engineered features such as HOG, Gabor feature and Hough transform do not work well for huge data of high resolution. Robust and computationally efficient systems are required which can learn presentations from the massive satellite imagery. In this paper, a target detection system for satellite imagery is proposed which uses EdgeBoxes and Convolutional Neural Network (CNN) for classifying target and non-target objects in a scene. The edge information of targets in satellite imagery contains very prominent and concise attributes. EdgeBoxes uses the edge information to filter the set of target proposals. CNN is a deep learning classifier with a high learning capacity and a capability of automatically learning optimum features from training data. Moreover, CNN is invariant to minor rotations and shifts in the target object. Encouraging experimental results have been obtained on a large dataset which shows the optimum performance and robustness of our system in complex scenes. Index Terms— Convolutional Neural Networks, Deep Learning, EdgeBoxes, Satellite Images, Target Detection. I. INTRODUCTION UTOMATIC detection of military targets such as oil tanks, aircrafts, artillery, etc. in high resolution satellite imagery has great significance in military applications. With the rapid development of satellite imaging and geographic information systems, a large amount of high resolution images can be acquired effortlessly from Google Earth. The non- hyperspectral image data has been used in many civil and military applications. Various techniques and features have been proposed so far for automatic target detection in satellite imagery. Zhang et al. [1] developed a hierarchical algorithm based on Adaboost classifier and HOG feature for detection of oil tanks. Han et al. in [2] proposed a method based on graph search strategy and improved Hough Transform for detection of oil tanks in satellite imagery. Yildiz et al. in [3] employed Gabor feature and used SVM classifier to detect different aircrafts. Gabor filter is also employed by authors in [4,5] for road crack detection in aerial images and settlement zone detection is satellite images respectively. Hsieh et al. in [6] employed Zernike moments, aircraft contour and wavelets and used SVM classifier for the detection of aircrafts in satellite images. Most of the methods discussed above use hand-crafted features and work effectively in their scenes only. Deep learning is a very effective method for learning optimum features directly from huge training dataset automatically. Now a day in numerous applications computer vision along with deep learning have outperformed humans. Furthermore, the use of Graphical Processing Units (GPUs) has decreased the training time of deep learning methods. Large databases of labelled data and pre-trained networks are now publicly available. The two popular models of deep learning are Deep Belief Network (DBN) [7] and Convolutional Neural Network (CNN) [8]. CNN is a modern deep learning method which is widely used for image recognition because it is invariant to small rotation and shifts [9]. DBN is a probabilistic generative model which is pre-trained as Restricted Boltzmann Machine layer by layer, and then finally tuned by back-propagation algorithm to become a classifier [9]. Chen et al. [10] employed object locating method along with DBN for aircraft detection in satellite images. Saliency has also been used for image classification by various researchers such as Li et al. [11] applied visual symmetry detection and saliency computation for aircraft detection in satellite images. Zhang et al. [12] and Sattar et al. [13] employed saliency and used unsupervised learning for image classification. Identification of fixation points, detection of image regions representing the scene and detection of dominant objects in a scene are the primary goals of saliency. Nevertheless, satellite images often contain several targets and correct localization of each target is required. Saliency cannot be directly employed for automatic target detection in satellite images and it needs the Automatic Target Detection in Satellite Images using Deep Learning Muhammad Jaleed Khan, Adeel Yousaf, Nizwa Javed, Shifa Nadeem, and Khurram Khurshid A Muhammad Jaleed Khan is with the Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan. (Phone: +92316-410- 1087; mjk093@gmail.com). Adeel Yousaf is with the Department of Avionics Engineering, Institute of Space Technology, Islamabad, Pakistan. (adeelyousaf1993@gmail.com). Nizwa Javed is with the Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan. (niz.jvd@gmail.com). Shifa Nadeem is with the Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan. (shifanadeem93@gmail.com). Khurram Khurshid is with the Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan. (khurram.khurshid@ist.edu.pk).