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Sensors 2015, 15(6), 13994-14015; doi:10.3390/s150613994

VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity

1
Centre for Automation and Robotics (CAR) CSIC-UPM, Ctra. Campo Real, Km. 0.2, La Poveda, Arganda del Rey, Madrid 28500, Spain
2
Faculty of Electrical Engineering, Technological University of Panama, Panama City 0819, Panama
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Pablo Gonzalez-de-Santos
Received: 17 April 2015 / Revised: 28 May 2015 / Accepted: 9 June 2015 / Published: 15 June 2015
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)

Abstract

Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations. View Full-Text
Keywords: ground bearing capacity; VIS-NIR; LWIR; SWIR; multispectral; soil moisture; optical filters; penetrometer; soil compaction ground bearing capacity; VIS-NIR; LWIR; SWIR; multispectral; soil moisture; optical filters; penetrometer; soil compaction
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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MDPI and ACS Style

Fernández, R.; Montes, H.; Salinas, C. VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity. Sensors 2015, 15, 13994-14015.

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