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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

Map of the correlation coefficient maxima obtained via FFT of image pairs that are rotated and scaled by the quantities reported on the plot axes. The white cross identifies the rotation angle and scale variation, which provides the maximum correlation function value.
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f9-sensors-12-10228: Map of the correlation coefficient maxima obtained via FFT of image pairs that are rotated and scaled by the quantities reported on the plot axes. The white cross identifies the rotation angle and scale variation, which provides the maximum correlation function value.

Mentions: Figure 9 reports the color map of the correlation coefficient maxima detected via 2-DFFT. 2-DFFT is applied to the pair of images, the first of which is consistently the same as the image reported in Figure 8(a), whereas the second image is obtained by rotating and scaling the image shown in Figure 8(b) by the values reported on the axes of Figure 9.


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

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

Map of the correlation coefficient maxima obtained via FFT of image pairs that are rotated and scaled by the quantities reported on the plot axes. The white cross identifies the rotation angle and scale variation, which provides the maximum correlation function value.
© Copyright Policy
Related In: Results  -  Collection

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

f9-sensors-12-10228: Map of the correlation coefficient maxima obtained via FFT of image pairs that are rotated and scaled by the quantities reported on the plot axes. The white cross identifies the rotation angle and scale variation, which provides the maximum correlation function value.
Mentions: Figure 9 reports the color map of the correlation coefficient maxima detected via 2-DFFT. 2-DFFT is applied to the pair of images, the first of which is consistently the same as the image reported in Figure 8(a), whereas the second image is obtained by rotating and scaling the image shown in Figure 8(b) by the values reported on the axes of Figure 9.

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