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Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications.

Lingua A, Marenchino D, Nex F - Sensors (Basel) (2009)

Bottom Line: Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions.The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model) generation.The Auto-Adaptive SIFT operator (A(2) SIFT) has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.

View Article: PubMed Central - PubMed

Affiliation: Politecnico di Torino, DITAG, C.so Duca degli Abruzzi, 24 - 10129, Torino, Italy; E-Mails: andrea.lingua@polito.it (A.L.); francesco.nex@polito.it (F.N.).

ABSTRACT
In the photogrammetry field, interest in region detectors, which are widely used in Computer Vision, is quickly increasing due to the availability of new techniques. Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions. Feature extraction and matching techniques, which are traditionally used in photogrammetry, are usually inefficient for these applications as they are unable to provide reliable results under extreme geometrical conditions (convergent taking geometry, strong affine transformations, etc.) and for bad-textured images. A performance analysis of the SIFT technique in aerial and close-range photogrammetric applications is presented in this paper. The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model) generation. First, the performances of the SIFT operator have been compared with those provided by feature extraction and matching techniques used in photogrammetry. All these techniques have been implemented by the authors and validated on aerial and terrestrial images. Moreover, an auto-adaptive version of the SIFT operator has been developed, in order to improve the performances of the SIFT detector in relation to the texture of the images. The Auto-Adaptive SIFT operator (A(2) SIFT) has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.

No MeSH data available.


Related in: MedlinePlus

Comparative test. Image correspondences automatically matched (L=10) by SIFT with D/(x̂)/=0.01 (a), D/(x̂)/=0.03 (b), A2 SIFT (c).
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f8-sensors-09-03745: Comparative test. Image correspondences automatically matched (L=10) by SIFT with D/(x̂)/=0.01 (a), D/(x̂)/=0.03 (b), A2 SIFT (c).

Mentions: The comparison of the auto-adaptive solution with the points extracted using D/(x̂)/ = 0.03 and D/(x̂/ = 0.01 is reported in Figure 8. Only one image of the stereopair is reported in order to evaluate the points' distribution.


Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications.

Lingua A, Marenchino D, Nex F - Sensors (Basel) (2009)

Comparative test. Image correspondences automatically matched (L=10) by SIFT with D/(x̂)/=0.01 (a), D/(x̂)/=0.03 (b), A2 SIFT (c).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3297131&req=5

f8-sensors-09-03745: Comparative test. Image correspondences automatically matched (L=10) by SIFT with D/(x̂)/=0.01 (a), D/(x̂)/=0.03 (b), A2 SIFT (c).
Mentions: The comparison of the auto-adaptive solution with the points extracted using D/(x̂)/ = 0.03 and D/(x̂/ = 0.01 is reported in Figure 8. Only one image of the stereopair is reported in order to evaluate the points' distribution.

Bottom Line: Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions.The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model) generation.The Auto-Adaptive SIFT operator (A(2) SIFT) has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.

View Article: PubMed Central - PubMed

Affiliation: Politecnico di Torino, DITAG, C.so Duca degli Abruzzi, 24 - 10129, Torino, Italy; E-Mails: andrea.lingua@polito.it (A.L.); francesco.nex@polito.it (F.N.).

ABSTRACT
In the photogrammetry field, interest in region detectors, which are widely used in Computer Vision, is quickly increasing due to the availability of new techniques. Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions. Feature extraction and matching techniques, which are traditionally used in photogrammetry, are usually inefficient for these applications as they are unable to provide reliable results under extreme geometrical conditions (convergent taking geometry, strong affine transformations, etc.) and for bad-textured images. A performance analysis of the SIFT technique in aerial and close-range photogrammetric applications is presented in this paper. The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model) generation. First, the performances of the SIFT operator have been compared with those provided by feature extraction and matching techniques used in photogrammetry. All these techniques have been implemented by the authors and validated on aerial and terrestrial images. Moreover, an auto-adaptive version of the SIFT operator has been developed, in order to improve the performances of the SIFT detector in relation to the texture of the images. The Auto-Adaptive SIFT operator (A(2) SIFT) has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.

No MeSH data available.


Related in: MedlinePlus