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Mosaicing of hyperspectral images: the application of a spectrograph imaging device.

Moroni M, Dacquino C, Cenedese A - Sensors (Basel) (2012)

Bottom Line: The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system.This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification.Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements.

View Article: PubMed Central - PubMed

Affiliation: DICEA-Sapienza University of Rome, via Eudossiana 18, Rome 00184, Italy. monica.moroni@uniroma1.it

ABSTRACT
Hyperspectral monitoring of large areas (more than 10 km(2)) can be achieved via the use of a system employing spectrometers and CMOS cameras. A robust and efficient algorithm for automatically combining multiple, overlapping images of a scene to form a single composition (i.e., for the estimation of the point-to-point mapping between views), which uses only the information contained within the images themselves is described here. The algorithm, together with the 2D fast Fourier transform, provides an estimate of the displacement between pairs of images by accounting for rotations and changes of scale. The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system. This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification. The performances of the algorithm were evaluated using sample images and image sequences acquired during a proximal sensing field campaign conducted in San Teodoro (Olbia-Tempio-Sardinia). The hyperspectral cube closely corresponds to the mosaic. Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements.

No MeSH data available.


Related in: MedlinePlus

Result of the mosaicing procedure applied to 500 images acquired with the camera Dalsa 4M60 equipped with a standard lens. For a better interpretation of the image, the entire mosaic has been split into two parts; the common portion is highlighted within a dashed rectangular area.
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f12-sensors-12-10228: Result of the mosaicing procedure applied to 500 images acquired with the camera Dalsa 4M60 equipped with a standard lens. For a better interpretation of the image, the entire mosaic has been split into two parts; the common portion is highlighted within a dashed rectangular area.

Mentions: The result of the application of the mosaicing code to a sequence of 500 images (extracted from the sequence of 1,500 images) acquired during the monitoring campaign is presented in Figure 12. For a better interpretation of the image, the entire mosaic has been split into two parts; the portion common to both images is highlighted within the dashed rectangular area. The use of a high spatial resolution acquisition sensor and a flight height of less than 500 m yields a ground resolution of less than 10 cm. The region represents an area recently affected by the construction of a residential area and a rural environment characterized by shrub vegetation mixed with large areas of lawn.


Mosaicing of hyperspectral images: the application of a spectrograph imaging device.

Moroni M, Dacquino C, Cenedese A - Sensors (Basel) (2012)

Result of the mosaicing procedure applied to 500 images acquired with the camera Dalsa 4M60 equipped with a standard lens. For a better interpretation of the image, the entire mosaic has been split into two parts; the common portion is highlighted within a dashed rectangular area.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472825&req=5

f12-sensors-12-10228: Result of the mosaicing procedure applied to 500 images acquired with the camera Dalsa 4M60 equipped with a standard lens. For a better interpretation of the image, the entire mosaic has been split into two parts; the common portion is highlighted within a dashed rectangular area.
Mentions: The result of the application of the mosaicing code to a sequence of 500 images (extracted from the sequence of 1,500 images) acquired during the monitoring campaign is presented in Figure 12. For a better interpretation of the image, the entire mosaic has been split into two parts; the portion common to both images is highlighted within the dashed rectangular area. The use of a high spatial resolution acquisition sensor and a flight height of less than 500 m yields a ground resolution of less than 10 cm. The region represents an area recently affected by the construction of a residential area and a rural environment characterized by shrub vegetation mixed with large areas of lawn.

Bottom Line: The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system.This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification.Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements.

View Article: PubMed Central - PubMed

Affiliation: DICEA-Sapienza University of Rome, via Eudossiana 18, Rome 00184, Italy. monica.moroni@uniroma1.it

ABSTRACT
Hyperspectral monitoring of large areas (more than 10 km(2)) can be achieved via the use of a system employing spectrometers and CMOS cameras. A robust and efficient algorithm for automatically combining multiple, overlapping images of a scene to form a single composition (i.e., for the estimation of the point-to-point mapping between views), which uses only the information contained within the images themselves is described here. The algorithm, together with the 2D fast Fourier transform, provides an estimate of the displacement between pairs of images by accounting for rotations and changes of scale. The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system. This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification. The performances of the algorithm were evaluated using sample images and image sequences acquired during a proximal sensing field campaign conducted in San Teodoro (Olbia-Tempio-Sardinia). The hyperspectral cube closely corresponds to the mosaic. Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements.

No MeSH data available.


Related in: MedlinePlus