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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
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.
목차
Abstract
1. Introduction
2. Study Sites and Materials
3. Methodology
4. Experimental Results
5. Quality Assessment and Discussion
6. Conclusion
References
참고문헌 (17)
참고문헌 신청
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 fractionsof endmembers: application to land–cover change in theBrazilian Amazon, Remote Sensing of Environment, Vol.52, No. 2, pp. 137-154.
Blaschke, T. (2010), Object based image analysis for remotesensing, ISPRS Journal of Photogrammetry and remotesensing, Vol. 65, No. 1, pp. 2-16.
Carter, G.A. (1991), Primary and secondary effects ofthe water content on the spectral reflectance of leaves,American Journal of Botany, Vol. 78, No. 7, pp. 916-924.
Chen, S., Chen, L., Liu, Y., and Li, X. (2013), Experimentalsimulation on mixed spectra of leaves and calcite forinversion 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-spectraldata analysis, International Journal of Remote Sensing,Vol. 25, No. 6, pp. 1153-1166.
Blaschke, T. (2010), Object based image analysis for remotesensing, ISPRS Journal of Photogrammetry and remotesensing, Vol. 65, No. 1, pp. 2-16.
Carter, G.A. (1991), Primary and secondary effects ofthe water content on the spectral reflectance of leaves,American Journal of Botany, Vol. 78, No. 7, pp. 916-924.
Chen, S., Chen, L., Liu, Y., and Li, X. (2013), Experimentalsimulation on mixed spectra of leaves and calcite forinversion 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-spectraldata analysis, International Journal of Remote Sensing,Vol. 25, No. 6, pp. 1153-1166.
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UCI(KEPA) : I410-ECN-0101-2016-533-002817571