衛載SAR影像方位參數之求解，是以檔頭資料所提供之衛星位置為初始值代入平差模式進行求解，並加入數值地形模型資訊來建立物空間與像空間之關係，以求取地塊影像，本研究即以此地塊影像出發，以進行影像匹配之研究。
本研究試圖以方差與協方差分量加入影像匹配中，期許能改善其匹配精度。本研究以最佳不變二次無偏差估計式（BIQUE)進行方差與協方差分量估計，此法中最重要的步驟是進行分塊，在此採用類別間方差法（Between-class variance）。以此法將約化觀測量分為兩塊，在此會得到兩個方差分量和一個協方差分量，分別有其對應的陪伴矩陣，共三個陪伴矩陣。由方差與協方差分量及陪伴矩陣構成協方差矩陣，然後經由迭代過程達到收斂之後，可得修正後的協方差矩陣，將之求逆可得知各觀測量所對應的權矩陣，此權矩陣可利後續最小二乘匹配法使用。
實驗影像為Radarsat-1影像，加入BIQUE之後，在視窗大小9×9~13×13可改善匹配精度，RMS約可提升0.1 pixels。

The orientation parameters are solved from the satellite header files information and adjustment model. Then adding digital terrain model is in order to develop relations between object space and image space. In this study, we use the groundel area images to match.
The research studies on image matching by variance and covariance components, and expects to improve the image matching precision. The study uses the best invariant quadratic unbiased estimator (BIQUE). In this way, it is the most important to segment the observations. The research uses the between-class variance to do it. It separates the observations into two parts, so we can get two variance components and one covariance components. The variance and covariance components have its accompanying matrices, so we have three accompanying matrices. To assign the variance and covariance components and to adjust relative weights through iteration until a steady parameter state is reached. After that, the least-squares image matching can use the weights in order to improve on a conventional stochastic model.
The study uses the Radarsat-1 image to test. After weighting in BIQUE, the RMS improves about 0.1 pixels in window sizes between 9×9 to 13×13 pixels.

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