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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

Sample images acquired by (a) the 4M60 camera equipped with a standard lens and (b) the VIS spectral imaging device.
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f11-sensors-12-10228: Sample images acquired by (a) the 4M60 camera equipped with a standard lens and (b) the VIS spectral imaging device.

Mentions: The images used to build the mosaic show modest shading toward the periphery, which is caused by vignetting. This problem is modeled by a cos4(α) fall-off in intensity away from the principal point, assuming that the optic axis passes through the image center. No further image pre-processing was required. Figure 11(a) shows a sample image acquired by the 4M60 camera equipped with a standard lens and corrected for image noises. Figure 11(b) shows the corresponding image (along the dashed line in Figure 11(a)) acquired by the spectral imaging device for the visible range of the electromagnetic spectrum (400 nm to 1,000 nm). The size of imaged scene is determined by the width of the entrance aperture of the spectrograph and by the length of the slit [3]. NIR images from the field survey are unavailable, due to technical problems encountered during the measurement campaign. The spectral information is shown along the λ axis. Each column of the image is, therefore, representative of the reflectance characteristics at a given wavelength of the imaged portion passing through the spectrometer slit. Columns must be appropriately combined to reconstruct the image of the entire area at the given wavelength. This was accomplished by employing image mosaicing algorithm results.


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

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

Sample images acquired by (a) the 4M60 camera equipped with a standard lens and (b) the VIS spectral imaging device.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-12-10228: Sample images acquired by (a) the 4M60 camera equipped with a standard lens and (b) the VIS spectral imaging device.
Mentions: The images used to build the mosaic show modest shading toward the periphery, which is caused by vignetting. This problem is modeled by a cos4(α) fall-off in intensity away from the principal point, assuming that the optic axis passes through the image center. No further image pre-processing was required. Figure 11(a) shows a sample image acquired by the 4M60 camera equipped with a standard lens and corrected for image noises. Figure 11(b) shows the corresponding image (along the dashed line in Figure 11(a)) acquired by the spectral imaging device for the visible range of the electromagnetic spectrum (400 nm to 1,000 nm). The size of imaged scene is determined by the width of the entrance aperture of the spectrograph and by the length of the slit [3]. NIR images from the field survey are unavailable, due to technical problems encountered during the measurement campaign. The spectral information is shown along the λ axis. Each column of the image is, therefore, representative of the reflectance characteristics at a given wavelength of the imaged portion passing through the spectrometer slit. Columns must be appropriately combined to reconstruct the image of the entire area at the given wavelength. This was accomplished by employing image mosaicing algorithm results.

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