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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

Cross-correlation map corresponding to a rotation angle of −21° clockwise and a scale of 0.98 times the size of the original image. The image is 240 × 240 pixels instead of 256 × 256 pixels, due to the change of scale.
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f10-sensors-12-10228: Cross-correlation map corresponding to a rotation angle of −21° clockwise and a scale of 0.98 times the size of the original image. The image is 240 × 240 pixels instead of 256 × 256 pixels, due to the change of scale.

Mentions: The largest value in this map is highlighted with a white cross. The mosaicing algorithm is designed to select the rotation angle and the scale variation that provide the maximum value of the correlation function. In this case, the maximum corresponds to a rotation angle of −21° clockwise and a scale 0.98 times the size of the original image. As expected, this peak is shifted by 20 pixels in both the x and y directions (Figure 10).


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

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

Cross-correlation map corresponding to a rotation angle of −21° clockwise and a scale of 0.98 times the size of the original image. The image is 240 × 240 pixels instead of 256 × 256 pixels, due to the change of scale.
© Copyright Policy
Related In: Results  -  Collection

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

f10-sensors-12-10228: Cross-correlation map corresponding to a rotation angle of −21° clockwise and a scale of 0.98 times the size of the original image. The image is 240 × 240 pixels instead of 256 × 256 pixels, due to the change of scale.
Mentions: The largest value in this map is highlighted with a white cross. The mosaicing algorithm is designed to select the rotation angle and the scale variation that provide the maximum value of the correlation function. In this case, the maximum corresponds to a rotation angle of −21° clockwise and a scale 0.98 times the size of the original image. As expected, this peak is shifted by 20 pixels in both the x and y directions (Figure 10).

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