IMAGE PROCESSING, PATTERN RECOGNITION
A real-time semantic segmentation algorithm for aerial imagery
Yu. B. Blokhinov, V. A. Gorbachev, Yu. O. Rakutin, A. D. Nikitin
State Research Institute of Aviation Systems, Moscow, Russia
We propose a novel effective algorithm for real-time semantic segmentation of images that has the best accuracy in its class. Based on a comparative analysis of preliminary segmentation methods, methods for calculating attributes from image segments, as well as various algorithms of machine learning, the most effective methods in terms of their accuracy and performance are identified. Based on the research results, a modular near real-time algorithm of semantic segmentation is constructed. Training and testing is performed on the ISPRS Vaihingen collection of aerial photos of the visible and IR ranges, to which a pixel map of the terrain heights is attached. An original method for obtaining a normalized nDSM for the original DSM is proposed.
image analysis, pattern recognition, detection, classification, aerial images, DSM, superpixels, feature vector, semantic segmentation, machine learning, conditional random fields.
|Russian Foundation for Basic Research
|The work was partially funded by the Russian Foundation of Basic Research, grant No. 17-08-00191 à. The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF): http://www.ifp.unistuttgart.de/dgpf/DKEP-Allg.html.
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Yu. B. Blokhinov, V. A. Gorbachev, Yu. O. Rakutin, A. D. Nikitin, “A real-time semantic segmentation algorithm for aerial imagery”, Computer Optics, 42:1 (2018), 141–148
Citation in format AMSBIB
\by Yu.~B.~Blokhinov, V.~A.~Gorbachev, Yu.~O.~Rakutin, A.~D.~Nikitin
\paper A real-time semantic segmentation algorithm for aerial imagery
\jour Computer Optics
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