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.
Muhammad Jaleed Khan, Adeel Yousaf, Nizwa Javed, Shifa Nadeem, Khurram Khurshid. (2017) Automatic Target Detection in Satellite Images using Deep Learning, Journal of Space Technology , Volume 7, Issue 1.
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