Abstract
This article considers the problem of constructing a sequential computational procedure for detecting artificial changes of remote sensing data (RSD) using a set of elementary algorithms of detecting artificial RSD changes. The stated task has been solved within the framework of the passive approach, which requires determining actual changes (forgeries) in RSD based on computer analysis.
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This paper uses the materials of the report submitted at the 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies,” Samara, Russia, September 23–28, 2013.
Andrei Vladimirovich Kuznetsov. Born 1987. Graduated with honors from the Samara State Aerospace University in 2010. Received candidate’s degree in 2013. Scientific interests: image processing and analysis, detection of local artificial changes in images, pattern recognition, and geoinformatics. Author of 25 papers. Junior research fellow at the Institute of Image Processing Systems, Russian Academy of Sciences.
Vladislav Valer’evich Myasnikov. Born 1971. Graduated from Samara State Aerospace University in 1994. Received candidate’s degree in 1998 and doctoral degree in 2008. Scientific interests: signal and image digital processing, computer vision, pattern recognition, artificial intelligence, and geoinformatics. Author of more than 100 papers. Member of the Russian Association of Pattern Recognition and Image Analysis.
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Kuznetsov, A.V., Myasnikov, V.V. Sequential computational procedure for remote sensing data forgery detection. Pattern Recognit. Image Anal. 25, 645–653 (2015). https://doi.org/10.1134/S1054661815040136
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DOI: https://doi.org/10.1134/S1054661815040136