• Title/Summary/Keyword: Linear pushbroom

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Precision correction of satellite-based linear pushbroom-type CCD camera images (선형 CCD카메라 영상의 정밀 기하학적 보정)

  • 신동석;이영란;이흥규
    • Korean Journal of Remote Sensing
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    • v.14 no.2
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    • pp.137-148
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    • 1998
  • An algorithm developed for the precision correction of high resolution satellite images is introduced in this paper. In general, the polynomial warping algorithm which derives polynomial equations between GCPs extracted from an image and a base map requires many GCPs well-distributed over the image. The precision correction algorithm described in this paper is based on a sensor-orbit-Earth geometry, and therefore, it is capable of correcting a raw image using only 2-3 GCPs. This algorithm estimates the errors on the orbit determination and the attitude of the satellite by using a Kalman filter. This algorithm was implemented, tested and integrated into the KITSAT-3 image preprocessing software.

RPC Model Generation from the Physical Sensor Model (영상의 물리적 센서모델을 이용한 RPC 모델 추출)

  • Kim, Hye-Jin;Kim, Jae-Bin;Kim, Yong-Il
    • Journal of Korean Society for Geospatial Information Science
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    • v.11 no.4 s.27
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    • pp.21-27
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    • 2003
  • The rational polynomial coefficients(RPC) model is a generalized sensor model that is used as an alternative for the physical sensor model for IKONOS-2 and QuickBird. As the number of sensors increases along with greater complexity, and as the need for standard sensor model has become important, the applicability of the RPC model is also increasing. The RPC model can be substituted for all sensor models, such as the projective camera the linear pushbroom sensor and the SAR This paper is aimed at generating a RPC model from the physical sensor model of the KOMPSAT-1(Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects $510{\sim}730nm$ panchromatic images with a ground sample distance (GSD) of 6.6m and a swath width of 17 km by pushbroom scanning. We generated the RPC from a physical sensor model of KOMPSAT-1 and aerial photography. The iterative least square solution based on Levenberg-Marquardt algorithm is used to estimate the RPC. In addition, data normalization and regularization are applied to improve the accuracy and minimize noise. And the accuracy of the test was evaluated based on the 2-D image coordinates. From this test, we were able to find that the RPC model is suitable for both KOMPSAT-1 and aerial photography.

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