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A Multispectral Image Creating Method for a New Airborne Four-Camera System with Different Bandpass Filters.

Li H, Zhang A, Hu S - Sensors (Basel) (2015)

Bottom Line: For this multispectral system, an automatic multispectral data composing method was proposed.For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system.Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of 3D Information Acquisition and Application of Ministry, Capital Normal University, Beijing 100048, China. lihanlun@126.com.

ABSTRACT
This paper describes an airborne high resolution four-camera multispectral system which mainly consists of four identical monochrome cameras equipped with four interchangeable bandpass filters. For this multispectral system, an automatic multispectral data composing method was proposed. The homography registration model was chosen, and the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) were used to generate matching points. For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system. Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels.

No MeSH data available.


The true-color composite (red, green, blue); (a) the unregistered multispectral image; (b) the enlarged partial region of (a); (c) the registered multispectral image; (d) the enlarged partial region of (c).
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sensors-15-17453-f012: The true-color composite (red, green, blue); (a) the unregistered multispectral image; (b) the enlarged partial region of (a); (c) the registered multispectral image; (d) the enlarged partial region of (c).

Mentions: In this experiment, four images containing a lot of man-made objects are used, as shown in Figure 4. Because it contains a lot of man-made objects, the SIFT operator can extract enough effective feature points, and the correct rate is higher than 50%, so the RANSAC can be used directly. Using the homography transformation model and the nearest neighbor interpolation method mapping the blue band, the red band, and the near infrared band to the green band, a four-band multispectral image is obtained. Figure 12 and Figure 13 depict the true-color composite and the CIR composite of the four-band image respectively. Figure 12a and Figure 13a depict the unregistered multispectral image, and Figure 12b and Figure 13b, respectively, display their enlarged partial regions. Figure 12c and Figure 13c depict the registered multispectral images, and Figure 12d and Figure 13d displays their enlarged partial regions. There are severe dislocations between different bands of the unregistered multispectral image. In contrast, these dislocations disappear in the registered multispectral image.


A Multispectral Image Creating Method for a New Airborne Four-Camera System with Different Bandpass Filters.

Li H, Zhang A, Hu S - Sensors (Basel) (2015)

The true-color composite (red, green, blue); (a) the unregistered multispectral image; (b) the enlarged partial region of (a); (c) the registered multispectral image; (d) the enlarged partial region of (c).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541943&req=5

sensors-15-17453-f012: The true-color composite (red, green, blue); (a) the unregistered multispectral image; (b) the enlarged partial region of (a); (c) the registered multispectral image; (d) the enlarged partial region of (c).
Mentions: In this experiment, four images containing a lot of man-made objects are used, as shown in Figure 4. Because it contains a lot of man-made objects, the SIFT operator can extract enough effective feature points, and the correct rate is higher than 50%, so the RANSAC can be used directly. Using the homography transformation model and the nearest neighbor interpolation method mapping the blue band, the red band, and the near infrared band to the green band, a four-band multispectral image is obtained. Figure 12 and Figure 13 depict the true-color composite and the CIR composite of the four-band image respectively. Figure 12a and Figure 13a depict the unregistered multispectral image, and Figure 12b and Figure 13b, respectively, display their enlarged partial regions. Figure 12c and Figure 13c depict the registered multispectral images, and Figure 12d and Figure 13d displays their enlarged partial regions. There are severe dislocations between different bands of the unregistered multispectral image. In contrast, these dislocations disappear in the registered multispectral image.

Bottom Line: For this multispectral system, an automatic multispectral data composing method was proposed.For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system.Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of 3D Information Acquisition and Application of Ministry, Capital Normal University, Beijing 100048, China. lihanlun@126.com.

ABSTRACT
This paper describes an airborne high resolution four-camera multispectral system which mainly consists of four identical monochrome cameras equipped with four interchangeable bandpass filters. For this multispectral system, an automatic multispectral data composing method was proposed. The homography registration model was chosen, and the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) were used to generate matching points. For the difficult registration problem between visible band images and near-infrared band images in cases lacking manmade objects, we presented an effective method based on the structural characteristics of the system. Experiments show that our method can acquire high quality multispectral images and the band-to-band alignment error of the composed multiple spectral images is less than 2.5 pixels.

No MeSH data available.