Identification of inpainted satellite images using evalutionary artificial neural network (EANN) and k-nearest neighbor(KNN) algorithm
Luqman Ali, Teerasit Kasetkasem, Faisal Ghaffar Khan, Thitiporn Chanwimaluang, Hiroki Nakahara · 2017
Now a day's recognition of satellite image authenticity has received too much attention due to the invention of various remote sensing image inpainting algorithms. Satellite image forgery can be referred as a technique in which fake satellite image is generated by the creation and alternation of new image contents. This paper proposes an algorithm for the identification of inpainted remote sensing images. The proposed algorithm is the connection of two major processes: feature extraction followed by use of classifiers based on k-nearest neighbor's algorithm (KNN) and evolutionary artificial neural networks algorithm (EANN) separately. The proposed algorithm can efficiently identify whether a satellite image is inpainted or not. The experimental results reveal that the proposed method has better performance with faster speed for various kind of satellite inpainting images.