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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

Comparison of imposed and measured displacements.
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f7-sensors-12-10228: Comparison of imposed and measured displacements.

Mentions: Figure 7 indicates the robustness of the method; i.e., this figure can be used to determine the accuracy of the displacement inferred by the correlation function peak to the imposed one. The committed error is where the imposed displacement components are (dx,dy) and the displacement detected by the algorithm components are (ix,iy). It is worth recalling the uncertainty on the displacement measurement is of ±0.5 pixel. Figure 7(a) demonstrates that the proposed methodology works properly when images are shifted with respect to one another. For all translations in the x, y and oblique (45°) directions, the displacement inferred based on the correlation coefficient peak is equal to the imposed displacement. The bisector of the coordinate plane coincides with the three lines displayed in Figure 7(a) (the bisector is not shown to avoid confusion).


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

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

Comparison of imposed and measured displacements.
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-12-10228: Comparison of imposed and measured displacements.
Mentions: Figure 7 indicates the robustness of the method; i.e., this figure can be used to determine the accuracy of the displacement inferred by the correlation function peak to the imposed one. The committed error is where the imposed displacement components are (dx,dy) and the displacement detected by the algorithm components are (ix,iy). It is worth recalling the uncertainty on the displacement measurement is of ±0.5 pixel. Figure 7(a) demonstrates that the proposed methodology works properly when images are shifted with respect to one another. For all translations in the x, y and oblique (45°) directions, the displacement inferred based on the correlation coefficient peak is equal to the imposed displacement. The bisector of the coordinate plane coincides with the three lines displayed in Figure 7(a) (the bisector is not shown to avoid confusion).

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