Advanced SearchSearch Tips
Generation of Simulated Geospatial Images from Global Elevation Model and SPOT Ortho-Image
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Generation of Simulated Geospatial Images from Global Elevation Model and SPOT Ortho-Image
Park, Wan Yong; Eo, Yang Dam;
  PDF(new window)
With precise sensor position, attitude element, and imaging resolution, a simulated geospatial image can be generated. In this study, a satellite image is simulated using SPOT ortho-image and global elevation data, and the geometric similarity between original and simulated images is analyzed. Using a SPOT panchromatic image and high-density elevation data from a 1/5K digital topographic map data an ortho-image with 10-meter resolution was produced. The simulated image was then generated by exterior orientation parameters and global elevation data (SRTM1, GDEM2). Experimental results showed that (1) the agreement of the image simulation between pixel location from the SRTM1/GDEM2 and high-resolution elevation data is above 99% within one pixel; (2) SRTM1 is closer than GDEM2 to high-resolution elevation data; (3) the location of error occurrence is caused by the elevation difference of topographical objects between high-density elevation data generated from the Digital Terrain Model (DTM) and Digital Surface Model (DSM)-based global elevation data. Error occurrences were typically found at river boundaries, in urban areas, and in forests. In conclusion, this study showed that global elevation data are of practical use in generating simulated images with 10-meter resolution.
Simulated Geospatial Images;SPOT Ortho-images;Global Elevation Data;Exterior Orientation Parameters;
 Cited by
대기보정된 Landsat TM 영상으로부터 모의영상 제작,이수봉;라푸히엔;어양담;편무욱;

한국측량학회지, 2015. vol.33. 1, pp.1-9 crossref(new window)
Analysis on the applicability of simulated image from SPOT 4 HRVIR image, KSCE Journal of Civil Engineering, 2017, 21, 4, 1434  crossref(new windwow)
Ji, L. and Gallo, K. (2006), An agreement coefficient for image comparison, Photogrammetric Engineering & Remote Sensing, Vol. 72, No. 7, pp. 823-833. crossref(new window)

Kim, S., Eo, Y., Lee, B., Jang, I., and Han, S. (2013), Remote sensed image simulation methodology, Journal of Next Generation Information Technology(JNIT), Vol.4, No.8, pp. 111-117.

Park, G. (2000), Analysis of the Sensor Characteristics and Imaging Time for Mapping, Agency for Defense Development, Daejeon, Korea, TEDC-409-000570.

Schott, J., Salvaggio, C., Brown, S., and Rose, R. (1995), Incorporation of texture in multispectral synthetic image generation tools, SPIE, Vol. 2469, pp. 189-196.

Seidel, K. and Datcu, M. (1993), Fusion of real and synthetic images for remote sensing scene understanding, FRACTAL '93, Kingston, UK, pp. 1-12.

Stark, R. (1993), Synthetic Image Generator Model:Application of View Angle Dependent Reflectivity Components and Performance Evaluation in the Visible Region, Master's thesis of Science in the Center for Imaging Science, Colleage of Imaging Arts and Sciences of the Rochester Institute of Technology, New York, USA, 241p.

Tachikawa, T., Kaku, M., Iwasaki, A., Gesch, D., Omioen, M., Zhang, Z., Danielson, J., Kreiger, T., Curtis, B., Haase, J., Abrams, M., Crippen, R., and Carabaijal, C. (2011), ASTER Global Digital Elevation Model Version 2- Summary of Validation Results, ASTER GDEM Validation Team, Japan and USA, pp. 1-27.

Woo, D. (2013), Quantitative assessment of 3D reconstruction procedure using stereo matching, Journal of IKEEE. Vol.17, No.1, pp. 001-009. crossref(new window)

Yun, Y., Cho, W., Park, J., and Lee, J. (2002), A Study on the generation of simulated high-resolution satellite images, Korean Journal of Remote Sensing, Vol. 18, No.6, pp. 327-336.