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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

Images utilised as inputs for the registration procedure. (a) Monochrome image acquired by the AVT Prosilica GC2450; (b) SWIR image; (c) LWIR thermal image.
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sensors-15-13994-f004: Images utilised as inputs for the registration procedure. (a) Monochrome image acquired by the AVT Prosilica GC2450; (b) SWIR image; (c) LWIR thermal image.

Mentions: The first phase of the experimental stage was devoted to the acquisition of images for implementing the registration procedure. Although the VIS-NIR, LWIR and SWIR cameras that constitute the proposed multisensory system are utilised for acquiring images from the same scene, resulting images are taken from slightly different viewpoints, with a different field of view and different resolution. Thus, this procedure aims to obtain a direct correspondence between the pixels of the different images before continuing with further processing steps for estimating the ground bearing capacity. The dataset acquired in this first phase included monochrome, LWIR and SWIR images. A chessboard pattern was utilised for facilitating the process of finding the point correspondences. Figure 4 shows an example of the monochrome, LWIR and SWIR images utilised as inputs for the registration procedure, whereas Figure 5 displays the resulting outputs of the aforementioned procedure.


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

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

Images utilised as inputs for the registration procedure. (a) Monochrome image acquired by the AVT Prosilica GC2450; (b) SWIR image; (c) LWIR thermal image.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-13994-f004: Images utilised as inputs for the registration procedure. (a) Monochrome image acquired by the AVT Prosilica GC2450; (b) SWIR image; (c) LWIR thermal image.
Mentions: The first phase of the experimental stage was devoted to the acquisition of images for implementing the registration procedure. Although the VIS-NIR, LWIR and SWIR cameras that constitute the proposed multisensory system are utilised for acquiring images from the same scene, resulting images are taken from slightly different viewpoints, with a different field of view and different resolution. Thus, this procedure aims to obtain a direct correspondence between the pixels of the different images before continuing with further processing steps for estimating the ground bearing capacity. The dataset acquired in this first phase included monochrome, LWIR and SWIR images. A chessboard pattern was utilised for facilitating the process of finding the point correspondences. Figure 4 shows an example of the monochrome, LWIR and SWIR images utilised as inputs for the registration procedure, whereas Figure 5 displays the resulting outputs of the aforementioned procedure.

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