行星TDI-CCD（Time Delayed Integration）影像行间积分时间的变化，使得原始影像难以直接使用有理多项式模型Rational Function Model (RFM)进行定位。本文按行等时间曝光假设对原始影像进行重构，从原始影像上查寻与重构影像行在成像时间上最接近的两影像行，对应列像元单线性插值重采样重构新的影像行。利用原始影像的卫星轨道姿态和传感器参数，建立重构影像的严密模型，拟合重构影像有理多项式系数Rational Polynomial Coefficient(RPC)。5景火星MEX-HRSC全色多视立体影像（纬度跨10.9度）和2景月球嫦娥二号立体影像（纬度跨8.3度）试验表明，重构影像的RPC拟合精度、重构影像RFM模型与原始影像严密定位模型相对精度达到0.01像素。本文方法避免了已有虚拟成像方法光路在原始影像和虚拟影像严密模型间的转换计算，以及两次重采样导致的辐射精度损失，为TDI-CCD变时间积分影像重构和RPC拟合提供了一种更为便捷的高精度处理方法。

The variation in integration time between lines in planetary TDI-CCD (Time Delayed Integration) images makes it difficult to directly use the Rational Function Model (RFM) for geolocation. This paper proposes a method based on the assumption of uniform exposure time across each image line in virtual imaging scenarios. By identifying the two adjacent original image lines that are temporally closest to each reconstructed line and employing linear interpolation for the respective column pixels, we reconstruct all new image lines. Using the satellite orbital attitude and sensor parameters of the original image, a rigorous model for the reconstructed image is established, and the Rational Polynomial Coefficient (RPC) is fitted for the reconstructed image. Our experiments, conducted on five panchromatic multi-view stereo images from the Mars Express High Resolution Stereo Camera (HRSC) and two stereo images from the Chang'e-2 lunar mission, demonstrate that the RPC fitting accuracy and the relative accuracy between the RFM models of the reconstructed images and rigorous models of the original images both achieve a precision of 0.01 pixels. This method avoids the transformation calculations between the ray paths of the original and virtual images, as well as the potential loss in radiometric accuracy due to twice resampling in existing virtual imaging methods, thereby providing a more convenient processing method for images reconstructing and RPCs fitting for TDI-CCD images with non-uniform integration times. Key words: planetary photogrammetry; TDI-CCD; virtual imaging; rigorous geometric model; RFM;lunar remote sensing mapping; mar remote sensing mapping.