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Remote Sensing Approach for Terramec...

Ewing, Jordan J.,

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Remote Sensing Approach for Terramechanics Applications Utilizing Machine and Deep Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Remote Sensing Approach for Terramechanics Applications Utilizing Machine and Deep Learning // Jordan J Ewing.
Author:	Ewing, Jordan J.,
Description:	1 electronic resource (128 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Remote sensing. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30632595
ISBN:	9798380184687

Remote Sensing Approach for Terramechanics Applications Utilizing Machine and Deep Learning /
Ewing, Jordan J.,

Remote Sensing Approach for Terramechanics Applications Utilizing Machine and Deep Learning /Jordan J Ewing. - 1 electronic resource (128 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Terrain traversability is critical for developing Go/No Go maps, significantly impacting a mission's success. To predict the mobility of a vehicle over a terrain, one must understand the soil characteristics. In situ measurements performed by soldiers in the field are the current method of collecting this information, which is time-consuming, are only point measurements, and can put soldiers in harm's way. Therefore, this study investigates using remote sensing as an alternative approach to characterize terrain properties. This approach will explore the relationships between electromagnetic radiation and soil types with varying properties. Optical, thermal, and hyperspectral sensors will be used to collect remote data and compare it against ground truth measurements for validation. Machine learning (linear, ridge, lasso, partial least squares, support vector machines, and k nearest neighbors) and deep learning (multi-layer perceptron and convolutional neural network) algorithms will be used to build prediction models.Results showed that soil properties such as soil gradation, moisture content, and soil strength measured by a geogauge and averaged cone penetrometer for 0-6" and 0-12" (CP06 and CP12) can be estimated remotely. Deep learning provides the best models for estimating terrain characteristics compared to machine learning. It is shown that this method can produce much finer spatial resolution coverage than traditional geospatial point-based interpolation approaches and yield a higher prediction accuracy. Predictions maps can be used to generate threshold-based Go / No Go maps using a vehicle cone index or as a cost map for vehicle performance. 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 rear wheel slip and CP12 and vehicle speed.This study demonstrates the potential of using remote sensing data for more rapid and finer spatial resolution predictions of terrain properties with higher accuracies compared to traditional in situ mapping methods implementing machine and deep learning algorithms. The remote sensing approach allows the generation of Go/No Go and vehicle cost maps and, most importantly, provides a safe alternative to keep soldiers out of harm's way.

English

ISBN: 9798380184687Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Hyperspectral imaging

Remote Sensing Approach for Terramechanics Applications Utilizing Machine and Deep Learning /
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100 1 $a Ewing, Jordan J., $e author. $3 3829695
245 1 0 $a Remote Sensing Approach for Terramechanics Applications Utilizing Machine and Deep Learning / $c Jordan J Ewing.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (128 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
500 $a Advisors: Oommen, Thomas Committee members: Brown, Laura; Vitton, Stanley; Jayakumar, Paramsothy.
502 $b Ph.D. $c Michigan Technological University $d 2023.
520 $a Terrain traversability is critical for developing Go/No Go maps, significantly impacting a mission's success. To predict the mobility of a vehicle over a terrain, one must understand the soil characteristics. In situ measurements performed by soldiers in the field are the current method of collecting this information, which is time-consuming, are only point measurements, and can put soldiers in harm's way. Therefore, this study investigates using remote sensing as an alternative approach to characterize terrain properties. This approach will explore the relationships between electromagnetic radiation and soil types with varying properties. Optical, thermal, and hyperspectral sensors will be used to collect remote data and compare it against ground truth measurements for validation. Machine learning (linear, ridge, lasso, partial least squares, support vector machines, and k nearest neighbors) and deep learning (multi-layer perceptron and convolutional neural network) algorithms will be used to build prediction models.Results showed that soil properties such as soil gradation, moisture content, and soil strength measured by a geogauge and averaged cone penetrometer for 0-6" and 0-12" (CP06 and CP12) can be estimated remotely. Deep learning provides the best models for estimating terrain characteristics compared to machine learning. It is shown that this method can produce much finer spatial resolution coverage than traditional geospatial point-based interpolation approaches and yield a higher prediction accuracy. Predictions maps can be used to generate threshold-based Go / No Go maps using a vehicle cone index or as a cost map for vehicle performance. 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 rear wheel slip and CP12 and vehicle speed.This study demonstrates the potential of using remote sensing data for more rapid and finer spatial resolution predictions of terrain properties with higher accuracies compared to traditional in situ mapping methods implementing machine and deep learning algorithms. The remote sensing approach allows the generation of Go/No Go and vehicle cost maps and, most importantly, provides a safe alternative to keep soldiers out of harm's way.
546 $a English
590 $a School code: 0129
650 4 $a Remote sensing. $3 535394
650 4 $a Geological engineering. $3 2122713
650 4 $a Geographic information science. $3 3432445
653 $a Hyperspectral imaging
653 $a Machine learning
653 $a Mobility
653 $a Terramechanics
653 $a Thermal imaging
690 $a 0799
690 $a 0466
690 $a 0370
710 2 $a Michigan Technological University. $b Geological/Mining Engineering & Sciences. $e degree granting institution. $3 3829673
720 1 $a Oommen, Thomas $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-03B.
790 $a 0129
791 $a Ph.D.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30632595