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ISPRS Int. J. Geo-Inf. 2018, 7(11), 430; https://doi.org/10.3390/ijgi7110430

Use of a Multilayer Perceptron to Automate Terrain Assessment for the Needs of the Armed Forces

Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
Received: 7 October 2018 / Revised: 28 October 2018 / Accepted: 4 November 2018 / Published: 6 November 2018
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Abstract

The classification of terrain in terms of passability plays a significant role in the process of military terrain assessment. It involves classifying selected terrain to specific classes (GO, SLOW-GO, NO-GO). In this article, the problem of terrain classification to the respective category of passability was solved by applying artificial neural networks (multilayer perceptron) to generate a continuous Index of Passability (IOP). The neural networks defined this factor for primary fields in two sizes (1000 × 1000 m and 100 × 100 m) based on the land cover elements obtained from Vector Smart Map (VMap) Level 2 and Shuttle Radar Topography Mission (SRTM). The work used a feedforward neural network consisting of three layers. The paper presents a comprehensive analysis of the reliability of the neural network parameters, taking into account the number of neurons, learning algorithm, activation functions and input data configuration. The studies and tests carried out have shown that a well-trained neural network can automate the process of terrain classification in terms of passability conditions. View Full-Text
Keywords: terrain classification; cross country movement; artificial neural network; multilayer perceptron terrain classification; cross country movement; artificial neural network; multilayer perceptron
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Pokonieczny, K. Use of a Multilayer Perceptron to Automate Terrain Assessment for the Needs of the Armed Forces. ISPRS Int. J. Geo-Inf. 2018, 7, 430.

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