Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing

Authors

Jordan Ewing, Michigan Technological UniversityFollow
Thomas Oommen, Michigan Technological UniversityFollow
Jobin Thomas, Michigan Technological UniversityFollow
Anush Kasaragod, Michigan Technological UniversityFollow
Richard Dobson, MTRI, Inc.
Colin Brooks, MTRI, Inc.
Paramsothy Jayakumar, U.S. Army Tank Automotive Research, Development and Engineering Center
Michael Cole, U.S. Army Tank Automotive Research, Development and Engineering Center
Tulga Ersal, University of Michigan, Ann Arbor

Document Type

Article

Publication Date

6-2023

Department

Department of Geological and Mining Engineering and Sciences

Abstract

Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0–6” (CP06) (R2/RMSE = 0.95/67) and 0–12” depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms.

Publisher's Statement

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. Publisher’s version of record: https://doi.org/10.3390/s23125505

Publication Title

Sensors

Recommended Citation

Ewing, J., Oommen, T., Thomas, J., Kasaragod, A., Dobson, R., Brooks, C., Jayakumar, P., Cole, M., & Ersal, T. (2023). Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing. Sensors, 23(12). http://doi.org/10.3390/s23125505
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/17391

Version

Publisher's PDF