Assessment of the rectification accuracy of IKONOS imagery based on two‐dimensional models

Shaker, Ahmed; Shi, Wen Zhong; Barakat, Hazem F.

doi:10.1080/01431160512331316847

Cited by 16 publications

(6 citation statements)

References 2 publications

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“…Finally, eight evenly distributed CPs were used to evaluate the RMS error after rectifying the IKONOS image. A detailed description of the field survey and GCP collection can be found in [25,26].…”

Section: Experiments 4: 3d Rectification Of Ikonosmentioning

confidence: 99%

Improved parameter estimation of the line-based transformation model for remote sensing image registration

Shaker¹,

Easa²,

Yan³

2024

Preprint

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The line-based transformation model (LBTM), built upon the use of affine transformation, was previously proposed for image registration and image rectification. The original LBTM first utilizes the control line features to estimate six rotation and scale parameters and subsequently uses the control point(s) to retrieve the remaining two translation parameters. Such a mechanism may accumulate the error of the six rotation and scale parameters toward the two translation parameters. In this study, we propose the incorporation of a direct method to estimate all eight transformation parameters of LBTM simultaneously using least-squares adjustment. The improved LBTM method was compared with the original LBTM through using one synthetic dataset and three experimental datasets for satellite image 2D registration and 3D rectification. The experimental results demonstrated that the improved LBTM converges to a steady solution with two to three ground control points (GCPs) and five ground control lines (GCLs), whereas the original LBTM requires at least 10 GCLs to yield a stable solution. Keywords: image registration; image rectification; remote sensing; ground control lines; line-based transformation model

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Section: Experiments 4: 3d Rectification Of Ikonosmentioning

confidence: 99%

Improved parameter estimation of the line-based transformation model for remote sensing image registration

Shaker¹,

Easa²,

Yan³

2024

Preprint

Self Cite

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“…The study also concludes that the most important factor in 2D rectification is the quality of the GCPs rather than quantity. More details about the accuracy level of the reference images can be found in Shi and Shaker (2003), Shaker and Shi (2003), Shaker et al (2005), Hanley and Fraser (2001) and Fraser et al (2002).…”

Section: Data Setsmentioning

confidence: 99%

The Line‐Based Transformation Model (LBTM) for image‐to‐image registration of high‐resolution satellite image data

Shi

Shaker

2006

International Journal of Remote Sensing

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Image-to-image registration is a prerequisite step for many important applications in different disciplines. In the field of remote sensing, the launch of new high-resolution satellites raises the need for a fast and dynamic technique for image-to-image registration in order to obtain full benefit from these satellites, especially for applications such as Earth monitoring and map updating. This paper presents the utilization of the Line-Based Transformation Model (LBTM) for image-to-image registration following the successful use of the LBTM for the rectification of high-resolution satellite images using linear features as control features.The developed model is similar in structure to some other point-based transformation models. However, line segments on linear features are used first to recover the model transformation coefficients. Based on the recovered coefficients, the whole image is registered using the ordinary point-based form of the model. Line segments on linear features have been chosen due to their existence in images regardless of the type of the land-use covered by the images and because line segments can be easily extracted from the images. Moreover, control points may not exist or a complete match between points on images cannot be achieved. Different forms of the developed model are discussed and results using different high-resolution satellite images from both IKONOS and QuickBird satellites are presented. The experimental results of the new technique show high integrity of the new model and indicate that image-to-image registration by LBTM is reliable.

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“…A transformation model (non-rigorous models) from a 2D satellite image coordinate system to ground 3D object space is studied [16,17] , and the transformation model is applied to the geo-positioning of IKONOS imagery. In particular, the following two factors that affect the data quality control are investigated: a) Change in terrain heights, and b) the number of GCPs with 3D ground coordinates.…”

Section: Point-based Geometric Rectificationmentioning

confidence: 99%

Modeling uncertainty in geographic information and analysis

Shi

2008

Sci. China Ser. E-Technol. Sci.

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Uncertainty modeling and data quality for spatial data and spatial analyses are important topics in geographic information science together with space and time in geography, as well as spatial analysis. In the past two decades, a lot of efforts have been made to research the uncertainty modeling for spatial data and analyses. This paper presents our work in the research. In particular, four progresses in the research are given out: (a) from determinedness-to uncertainty-based representation of geographic objects in GIS; (b) from uncertainty modeling for static data to dynamic spatial analyses; (c) from modeling uncertainty for spatial data to models; and (d) from error descriptions to quality control for spatial data.uncertainty, quality control, spatial data, spatial analysis, spatial model

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Assessment of the rectification accuracy of IKONOS imagery based on two‐dimensional models

Cited by 16 publications

References 2 publications

Improved parameter estimation of the line-based transformation model for remote sensing image registration

Improved parameter estimation of the line-based transformation model for remote sensing image registration

The Line‐Based Transformation Model (LBTM) for image‐to‐image registration of high‐resolution satellite image data

Modeling uncertainty in geographic information and analysis

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