Automatic target detection in satellite images is a challenging problem due to the varying size, ... more 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.
Automatic target detection in satellite images is a challenging problem due to the varying size, ... more 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.
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articles by Nizwa Javed