&lt;title&gt;Automatic fusion of multiple-sensor and multiple-season images&lt;/title&gt;

Lutsiv, V. R.; Malyshev, I. A.; Pepelka, Vadim

doi:10.1117/12.436990

Cited by 9 publications

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“…Now the contour structural elements were detected in the pair of pre-registered fragments, as it was described in our previous papers 1,2,3 , and the elements were structurally matched using the modified transformation model described in (2). The 3D matching performed at different elevation levels was successful!…”

Section: Structural Matching Based On the Expected Geometry Of Scene mentioning

confidence: 99%

Hierarchical 3D structural matching in aerospace photographs and indoor scenes

Lutsiv

Potapov

Novikova³

et al. 2005

SPIE Proceedings

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The investigation presented in this article continues our long-term efforts directed towards the automatic structural matching of aerospace photographs. An efficient target independent hierarchical structural matching tool was described in our previous paper 1 , which, however, was aimed mostly for the analysis of 2D scenes. It applied the same geometric transformation model to the whole area of image, thus it was nice for the space photographs taken from rather high orbits, but it often failed in the cases when the sensors were positioned near the 3D scenes being observed. Different transformation models should be applied to different parts of images in the last case, and finding a correct separation of image into the areas of homogeneous geometric transformations was the main problem. Now we succeeded in separating the images of scenes into the surfaces of different objects on the base of their textural and spectral features, thus we have got a possibility of separate matching the sub-images corresponding to such objects applying different transformation model to each such sub-image. Some additional limitations were used in the course of such separation and matching. In particular, the a priory assumptions were applied in different cases about the possible geometry of scenes, rules of illumination and shadowing, thus the aerospace photographs, indoor scenes, or images of aircrafts were analyzed in slightly differing ways. However the additional limitations applied could be considered as very general and are worth to be used in a wide sphere of practical tasks. The automatic image analysis was successful in all considered practical cases.

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Section: Structural Matching Based On the Expected Geometry Of Scene mentioning

confidence: 99%