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.
Highlights
Land formation and cover are key factors that influence the conduct of military operations [1,2,3]
According to the elements of land cover, terrain is divided into three classes of passability: GO, SLOW-GO and NO-GO TERRAIN [6]
This mostly involves an experienced officer marking impassable areas. This method requires huge experience and skill, but is time consuming. For these reasons the present study investigates the use of Artificial Neural Networks (ANN) for automating the passability map construction process, to significantly improve and accelerate the entire process of terrain assessment
Summary
Land formation and cover are key factors that influence the conduct of military operations [1,2,3]. In accordance with the applicable standards [4,7], the analysis of passability should be done manually as an overlay on a topographical map This mostly involves an experienced officer marking impassable areas. This method requires huge experience and skill, but is time consuming For these reasons the present study investigates the use of Artificial Neural Networks (ANN) for automating the passability map construction process, to significantly improve and accelerate the entire process of terrain assessment. The proposed methodology used the Index of Passability (IOP) designation, which is a coefficient reflecting the degree of limitation of vehicular speed by land cover elements In this study, this index was determined on a continuous scale, from 0 (impassable terrain) to 1 ( passable terrain)
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