Accuracy evaluation of automated object recognition using multispectral aerial images and neural network

Dmitriy Mozgovoy; Volodymyr Hnatushenko; Volodymyr Vasyliev

doi:10.1117/12.2502905

9 August 2018 Accuracy evaluation of automated object recognition using multispectral aerial images and neural network

Dmitriy Mozgovoy, Volodymyr Hnatushenko, Volodymyr Vasyliev

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108060H (2018) https://doi.org/10.1117/12.2502905
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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).

Citation Download Citation

Dmitriy Mozgovoy, Volodymyr Hnatushenko, and Volodymyr Vasyliev "Accuracy evaluation of automated object recognition using multispectral aerial images and neural network", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060H (9 August 2018); https://doi.org/10.1117/12.2502905

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