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

Fernández R, Montes H, Salinas C - Sensors (Basel) (2015)

Bottom Line: Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming.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 Article: PubMed Central - PubMed

Affiliation: Centre for Automation and Robotics (CAR) CSIC-UPM, Ctra. Campo Real, Km. 0.2, La Poveda, Arganda del Rey, Madrid 28500, Spain. roemi.fernandez@car.upm-csic.es.

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.

No MeSH data available.


Related in: MedlinePlus

(a) Normalised SWIR image; (b) LWIR thermal image.
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sensors-15-13994-f011: (a) Normalised SWIR image; (b) LWIR thermal image.

Mentions: In order to validate the capabilities of the proposed system, a total of 10 scenes with variable soil water content conditions were acquired, processed and evaluated. Ground truth data was carefully collected with a penetrometer for each scene in order to carry out a quantitative assessment of the proposed solution. Figure 10, Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15 illustrate most of the intermediate results obtained from the different steps that make up the algorithm proposed for the estimation of the ground bearing capacity. Figure 10 and Figure 11 display the dataset of a scene acquired with the proposed multisensory system. This dataset includes a monochrome image, two filtered images acquired with band-pass filters whose centre wavelength are 624 and 950 nm, a normalised SWIR image and a LWIR thermal image.


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

Fernández R, Montes H, Salinas C - Sensors (Basel) (2015)

(a) Normalised SWIR image; (b) LWIR thermal image.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507653&req=5

sensors-15-13994-f011: (a) Normalised SWIR image; (b) LWIR thermal image.
Mentions: In order to validate the capabilities of the proposed system, a total of 10 scenes with variable soil water content conditions were acquired, processed and evaluated. Ground truth data was carefully collected with a penetrometer for each scene in order to carry out a quantitative assessment of the proposed solution. Figure 10, Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15 illustrate most of the intermediate results obtained from the different steps that make up the algorithm proposed for the estimation of the ground bearing capacity. Figure 10 and Figure 11 display the dataset of a scene acquired with the proposed multisensory system. This dataset includes a monochrome image, two filtered images acquired with band-pass filters whose centre wavelength are 624 and 950 nm, a normalised SWIR image and a LWIR thermal image.

Bottom Line: Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming.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 Article: PubMed Central - PubMed

Affiliation: Centre for Automation and Robotics (CAR) CSIC-UPM, Ctra. Campo Real, Km. 0.2, La Poveda, Arganda del Rey, Madrid 28500, Spain. roemi.fernandez@car.upm-csic.es.

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.

No MeSH data available.


Related in: MedlinePlus