• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.22 no.4
• /
• pp.331-338
• /
• 2004

This paper describes capability of utilizing ground control points(GCPs) obtained from 1:1,000 and 1:5,000 digital vector maps to correct image coordinates which have errors due to bais rational polynomial coefficient(RPC) of IKONOS Geo-level stereo images. The accuracy of the bias-corrected images was improved to approximately 4m and 2m in planimetry and height, respectively. The accuracy was also compared with results from using GCPs obtained by GPS surveying. In consequence, bias-compensated IKONOS sereo imagery was evaluated to satisfy generating topographic map 1:10,000.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.9
• /
• pp.1-6
• /
• 2019

One method for image recognition is template matching. In conventional template matching, the block matching algorithm (BMA) is performed while changing the two-dimensional translational displacement of the template within a given matching image. The template size and shape do not change during the BMA. Since only two-dimensional translational displacement is considered, the success rate decreases if the size and direction of the object do not match in the template and the matching image. In this paper, a variable is added to adjust the two-dimensional direction and size of the template, and the optimal value of the variable is automatically calculated in the block corresponding to each two-dimensional translational displacement. Using the calculated optimal value, the template is automatically transformed into an optimal template for each block. The matching error value of each block is then calculated based on the automatically deformed template. Therefore, a more stable result can be obtained for the difference in direction and size. For ease of use, this study focuses on designing the algorithm in a closed form that does not require additional information beyond the template image, such as distance information.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.6D
• /
• pp.1025-1032
• /
• 2006

Sensor modeling of high-resolution satellites is a prerequisite procedure for mapping and GIS applications. Sensor models, describing the geometric relationship between scene and object, are divided into two main categories, which are rigorous and approximate sensor models. A rigorous model is based on the actual geometry of the image formation process, involving internal and external characteristics of the implemented sensor. However, approximate models require neither a comprehensive understanding of imaging geometry nor the internal and external characteristics of the imaging sensor, which has gathered a great interest within photogrammetric communities. This paper described a comparison between rigorous and various approximate sensor models that have been used to determine three-dimensional positions, and proposed the appropriate sensor model in terms of the satellite imagery usage. Through the case study of using IKONOS satellite scenes, rigorous and approximate sensor models have been compared and evaluated for the positional accuracy in terms of acquirable number of ground controls. Bias compensated RFM(Rational Function Model) turned out to be the best among compared approximate sensor models, both modified parallel projection and parallel-perspective model were able to be modelled with a small number of controls. Also affine transformation, one of the approximate sensor models, can be used to determine the planimetric position of high-resolution satellites and perform image registration between scenes.