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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) Classification map; (b) Mask generated from the classification map.
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sensors-15-13994-f019: (a) Classification map; (b) Mask generated from the classification map.

Mentions: Figure 16, Figure 17, Figure 18, Figure 19, Figure 20 and Figure 21 depict the same intermediate steps and results described above for an additional scene characterised for exhibiting a different behaviour of the ground bearing capacity. Figure 16 and Figure 17 display the dataset of the 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 thermal image.


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

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

(a) Classification map; (b) Mask generated from the classification map.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-13994-f019: (a) Classification map; (b) Mask generated from the classification map.
Mentions: Figure 16, Figure 17, Figure 18, Figure 19, Figure 20 and Figure 21 depict the same intermediate steps and results described above for an additional scene characterised for exhibiting a different behaviour of the ground bearing capacity. Figure 16 and Figure 17 display the dataset of the 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 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