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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

512 × 512 pixel image used for testing of the mosaicing algorithm; an example is shown of a pair of sub-images employed for the algorithm test.
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f4-sensors-12-10228: 512 × 512 pixel image used for testing of the mosaicing algorithm; an example is shown of a pair of sub-images employed for the algorithm test.

Mentions: The algorithm was tested using the sample image shown in Figure 4. The goal of this test was to investigate the algorithm performance with image pairs cropped from the sample image with varied extents of overlap (from 50% to 100%). The effects of translation, rotation and change of scale of the second sub-image with respect to the first sub-image have been considered. For each test, the correlation peak magnitude and its shift from the map center have been stored to perform the comparisons.


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

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

512 × 512 pixel image used for testing of the mosaicing algorithm; an example is shown of a pair of sub-images employed for the algorithm test.
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-12-10228: 512 × 512 pixel image used for testing of the mosaicing algorithm; an example is shown of a pair of sub-images employed for the algorithm test.
Mentions: The algorithm was tested using the sample image shown in Figure 4. The goal of this test was to investigate the algorithm performance with image pairs cropped from the sample image with varied extents of overlap (from 50% to 100%). The effects of translation, rotation and change of scale of the second sub-image with respect to the first sub-image have been considered. For each test, the correlation peak magnitude and its shift from the map center have been stored to perform the comparisons.

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