Advanced SearchSearch Tips
High Spatial Resolution Satellite Image Simulation Based on 3D Data and Existing Images
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
High Spatial Resolution Satellite Image Simulation Based on 3D Data and Existing Images
La, Phu Hien; Jeon, Min Cheol; Eo, Yang Dam; Nguyen, Quang Minh; Lee, Mi Hee; Pyeon, Mu Wook;
  PDF(new window)
This study proposes an approach for simulating high spatial resolution satellite images acquired under arbitrary sun-sensor geometry using existing images and 3D (three-dimensional) data. First, satellite images, having significant differences in spectral regions compared with those in the simulated image were transformed to the same spectral regions as those in simulated image by using the UPDM (Universal Pattern Decomposition Method). Simultaneously, shadows cast by buildings or high features under the new sun position were modeled. Then, pixels that changed from shadow into non-shadow areas and vice versa were simulated on the basis of existing images. Finally, buildings that were viewed under the new sensor position were modeled on the basis of open library-based 3D reconstruction program. An experiment was conducted to simulate WV-3 (WorldView-3) images acquired under two different sun-sensor geometries based on a Pleiades 1A image, an additional WV-3 image, a Landsat image, and 3D building models. The results show that the shapes of the buildings were modeled effectively, although some problems were noted in the simulation of pixels changing from shadows cast by buildings into non-shadow. Additionally, the mean reflectance of the simulated image was quite similar to that of actual images in vegetation and water areas. However, significant gaps between the mean reflectance of simulated and actual images in soil and road areas were noted, which could be attributed to differences in the moisture content.
High Spatial Resolution Image Simulation;Universal Pattern Decomposition Method;3D Reconstruction;Worldview-3 Image;
 Cited by
Adams, J.B., Sabol, D.E., Kapos, V., Almeida Filho, R., Roberts, D.A., Smith, M.O., and Gillespie, A.R. (1995), Classification of multispectral images based on fractions of endmembers: application to land–cover change in the Brazilian Amazon, Remote Sensing of Environment, Vol. 52, No. 2, pp. 137-154. crossref(new window)

Blaschke, T. (2010), Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and remote sensing, Vol. 65, No. 1, pp. 2-16. crossref(new window)

Carter, G.A. (1991), Primary and secondary effects of the water content on the spectral reflectance of leaves, American Journal of Botany, Vol. 78, No. 7, pp. 916-924. crossref(new window)

Chen, S., Chen, L., Liu, Y., and Li, X. (2013), Experimental simulation on mixed spectra of leaves and calcite for inversion of carbonate minerals from EO-1 hyperion data, GIScience & Remote Sensing, Vol. 50, No. 6, pp. 690-703.

Daigo, M., Ono, A., Urabe, R., and Fujiwara, N. (2004), Pattern decomposition method for hyper-multi-spectral data analysis, International Journal of Remote Sensing, Vol. 25, No. 6, pp. 1153-1166. crossref(new window)

Doz, S., Briottet, X., Porez-Nadal, F., and Lachérade, S. (2010), Simulation of urban optical images from high spectral and spatial resolution multi-angular airborne acquisitions, IEEE International Geoscience and Remote Sensing Symposium, IGARSS, 25-30 July, Honolulu, pp. 3572-3575.

Hoffer, R.M. (1978), Biological and physical considerations in applying computer aided analysis techniques to remote sensor data, Remote sensing: The Quantitative Approach, pp. 227-289.

Jensen, J.R. (2007), Remote Sensing of the Environment: An Earth Resource Perspective 2nd Edition, Prentice Hall Series in Geographic Information Science, Pearson Prentice Hall Inc, New Jersey.

Lee, H.S. and Lee, K.S. (2015), Atmospheric correction problems with multi-temporal high spatial resolution images from different satellite sensors, Korean Journal of Remote Sensing, Vol. 31, No. 4, pp. 321-330. crossref(new window)

Liu, B., Zhang, L., Zhang, X., Zhang, B., and Tong, Q. (2009), Simulation of EO-1 hyperion data from ALI multispectral data based on the spectral reconstruction approach, Sensors, Vol. 9, No. 4, pp. 3090-3108. crossref(new window)

Muramatsu, K., Furumi, S., Fujiwara, N., Hayashi, A., Daigo, M., and Ochiai, F. (2000), Pattern decomposition method in the albedo space for Landsat TM and MSS data analysis, International Journal of Remote Sensing, Vol. 21, No. 1, pp. 99-119. crossref(new window)

Ratti, C. and Richens, P. (2004), Raster analysis of urban form, Environment and Planning B: Planning and Design, Vol. 31, No. 2, pp. 297-309. crossref(new window)

Richens, P. (1997), Image processing for urban scale environmental modelling, In Proceedings of the 5 th international IBPSA Conference: Building Simulation, IBPSA, 8-10 September, Prague, Czech Republic, pp. 163-171.

Schott, J.R., Brown, S.D., Raqueno, R.V., Gross, H.N., and Robinson, G. (1999), An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development, Canadian Journal of Remote Sensing, Vol. 25, No. 2, pp. 99-111. crossref(new window)

Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M., and Morcette, J.J. (1997), Second simulation of the satellite signal in the solar spectrum, 6S: an overview, IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, No. 3, pp. 675-686. crossref(new window)

Zhang, L., Furumi, S., Murumatsu, K., Fujiwara, N., Daigo, M., and Zhang, L. (2006), Sensor-independent analysis method for hyperspectral data based on the pattern decomposition method, International Journal of Remote Sensing, Vol. 27, No. 21, pp. 4899-4910. crossref(new window)

Zhu, X., Chen, J., Gao, F., Chen, X., and Masek, J.G. (2010), An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions, Remote Sensing of Environment, Vol. 114, No. 1, pp. 2610-2623. crossref(new window)