Targeted change detection in remote sensing images

Vladimir Ignatiev, Alexey Trekin, Viktor Lobachev, Georgy Potapov, Evgeny Burnaev

Recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images (so called targeted change detection). In this paper we propose a formal problem statement that allows to use effectively the deep learning approach to analyze time-dependent series of remote sensing images. We also introduce a new framework for the development of deep learning models for targeted change detection and demonstrate some cases of business applications it can be used for.

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