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Accuracy evaluation of automated object recognition using multispectral aerial images and neural network
Author(s): Dmitriy Mozgovoy; Volodymyr Hnatushenko; Volodymyr Vasyliev
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Paper Abstract

A methodology of accuracy evaluation of automated object recognition using sub-meter spatial resolution multispectral aerial images and neural network is proposed. The methodology is applied to detection of 5 land cover classes from visible and infrared images using a multilevel convolutional neural network (CNN). In this work the well-known indicators of accuracy classification have been chosen: the confusion matrix and Kappa coefficient. Image processing results are analyzed. It is shown that the recognized object boundaries are delineated with sufficiently high accuracy and classes are well separated. The results of testing confirmed sufficiently high qualitative and quantitative indicators of the developed methodology (classification accuracy, sustainability, reproducibility).

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060H (9 August 2018); doi: 10.1117/12.2502905
Show Author Affiliations
Dmitriy Mozgovoy, EOS Data Analytics, Inc. (United States)
Oles Gonchar Dnipro National Univ. (Ukraine)
Volodymyr Hnatushenko, EOS Data Analytics, Inc. (United States)
Oles Gonchar Dnipro National Univ. (Ukraine)
Volodymyr Vasyliev, EOS Data Analytics, Inc. (United States)
Oles Gonchar Dnipro National Univ. (Ukraine)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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