Matching of straight line segments from aerial stereo images of urban areas

Ok, Ali Özgün; Wegner, Jan Dirk; Heipke, Christian; Rottensteiner, Franz; Soergel, Uwe; Toprak, Vedat

doi:10.1016/j.isprsjprs.2012.09.003

Cited by 49 publications

(28 citation statements)

References 27 publications

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“…) of corner arms with flanking width (ɛ) as used in Ok et al (2012). Figure 2(a) shows a configuration of an edged corner feature and the concept of flanking regions.…”

Section: Edged Corner Feature Extraction From Imagementioning

confidence: 99%

Matching Aerial Images to 3d Building Models Based on Context-Based Geometric Hashing

Jung¹,

Bang²,

Sohn³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, WG I/3KEY WORDS: Registration, 3D Building Models, Aerial Imagery, Geometric Hashing, Model to Image Matching ABSTRACT:In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labourintensive manual registration process.

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“…) of corner arms with flanking width (ɛ) as used in Ok et al (2012). Figure 2(a) shows a configuration of an edged corner feature and the concept of flanking regions.…”

Section: Edged Corner Feature Extraction From Imagementioning

confidence: 99%

Matching Aerial Images to 3d Building Models Based on Context-Based Geometric Hashing

Jung¹,

Bang²,

Sohn³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…The method carries out LICFs matching based on epipolar constraints and region descriptor of LICFs. In Ok et al (2012), a pair-wise matching manner are proposed. The method only select pairs that are close to each other and that intersection angle smaller than a threshold at as reference line pairs, and apply seven pair-wise constraints to reduce matching candidate pairs, and the best matching pairs are selected based on a similarity score combining all the pairwise constraints.…”

Section: Introductionmentioning

confidence: 99%

Line Matching Algorithm for Aerial Image Combining image and object space similarity constraints

Wang

Wang²,

Li³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A new straight line matching method for aerial images is proposed in this paper. Compared to previous works, similarity constraints combining radiometric information in image and geometry attributes in object plane are employed in these methods. Firstly, initial candidate lines and the elevation values of lines projection plane are determined by corresponding points in neighborhoods of reference lines. Secondly, project reference line and candidate lines back forward onto the plane, and then similarity measure constraints are enforced to reduce the number of candidates and to determine the finial corresponding lines in a hierarchical way. Thirdly, "one-to-many" and "many-to-one" matching results are transformed into "one-to-one" by merging many lines into the new one, and the errors are eliminated simultaneously. Finally, endpoints of corresponding lines are detected by line expansion process combing with "image-object-image" mapping mode. Experimental results show that the proposed algorithm can be able to obtain reliable line matching results for aerial images.

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“…They can also be directly derived from existing geographic spatial data, such as a GIS database, large-scale maps, a terrestrial mobile mapping system, and so on, which can largely reduce the cost of ground control work [22][23][24][25]. Given these, line features have been widely used in photogrammetric activities, including space intersection and resection [23,25], triangulation [22,25], relative and exterior orientation [26][27][28], image matching and registration [22,29,30], camera calibration [22,31], etc.…”

Section: Introductionmentioning

confidence: 99%

Rigorous Line-Based Transformation Model Using the Generalized Point Strategy for the Rectification of High Resolution Satellite Imagery

Shi

2016

Remote Sensing

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Abstract:High precision geometric rectification of High Resolution Satellite Imagery (HRSI) is the basis of digital mapping and Three-Dimensional (3D) modeling. Taking advantage of line features as basic geometric control conditions instead of control points, the Line-Based Transformation Model (LBTM) provides a practical and efficient way of image rectification. It is competent to build the mathematical relationship between image space and the corresponding object space accurately, while it reduces the workloads of ground control and feature recognition dramatically. Based on generalization and the analysis of existing LBTMs, a novel rigorous LBTM is proposed in this paper, which can further eliminate the geometric deformation caused by sensor inclination and terrain variation. This improved nonlinear LBTM is constructed based on a generalized point strategy and resolved by least squares overall adjustment. Geo-positioning accuracy experiments with IKONOS, GeoEye-1 and ZiYuan-3 satellite imagery are performed to compare rigorous LBTM with other relevant line-based and point-based transformation models. Both theoretic analysis and experimental results demonstrate that the rigorous LBTM is more accurate and reliable without adding extra ground control. The geo-positioning accuracy of satellite imagery rectified by rigorous LBTM can reach about one pixel with eight control lines and can be further improved by optimizing the horizontal and vertical distribution of control lines.

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Matching of straight line segments from aerial stereo images of urban areas

Cited by 49 publications

References 27 publications

Matching Aerial Images to 3d Building Models Based on Context-Based Geometric Hashing

Matching Aerial Images to 3d Building Models Based on Context-Based Geometric Hashing

Line Matching Algorithm for Aerial Image Combining image and object space similarity constraints

Rigorous Line-Based Transformation Model Using the Generalized Point Strategy for the Rectification of High Resolution Satellite Imagery

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