| Project/Area Number |
14580440
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報システム学(含情報図書館学)
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| Research Institution | Hirosaki University |
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Principal Investigator |
IIKURA Yoshikazu Hirosaki University, Faculty of Science and Technology, Professor, 理工学部, 教授 (30109897)
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| Project Period (FY) |
2002 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2003: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2002: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Keywords | precise geometric correction / topographic effect correction / digital elevation model / landcover classification / sky view factor / view shed / spatial segmentation / mixed pixel / ランドサットTM / 精密幾何変換 / 放射量補正 / 現存植生図 / ベクターラスター変換 / 正射投影変換 / IDL / 照り返し効果 |
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| Research Abstract |
In satellite images over rugged terrain, we can find some topographic effects entangled with atmospheric effects. Without correcting these effects, it is difficult to evaluate the satellite images for land cover classification and multi-temporal monitoring of natural environment. In order to reproduce these topographic effects in computer, we need digital elevation model(DEM) with the same map projection and spatial resolution as the satellite image concerned. The DEM is also required for accurate ortho-rectification of the satellite Image.
In this research project, we systemize satellite image processing based on the principles of remote sensing, which is applied to Landsat TM images. The satellite images are automatically laid over the DEM by using the proposing geometric correction method which utilizes the simulated direct solar irradiance image. Advanced methods for topographic effect correction based on a physical model of satellite level radiance is also developed to a satellite image with high solar elevation.
We also propose to model land cover as a tesselated spatial structure and apply the spatial segmentation to the image first. Then, mixed pixels are determined as the boundary pixels with definite end members found in neighborhood.
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