標準最小二乘影像匹配法之函數模式通常擁有輻射平移和尺度參數、及幾何仿射參數。本文旨於改進傳統的隨機模式，不再視差分的灰階為獨立且分布相同的變數。尺度性方差與協方差分量各派定給處理後的影像區塊。經估計所得之(協)方差分量續用以重新定義觀測量的協方差矩陣，並迭代平差相對的權值，直至獲得穩定的參數值為止。理論上，本文所介紹的估計式(Blue-estimator)雷同於最佳不變二次無偏估計式。實務上藉兩幅Radarsat-1 合成口徑雷達影像，以探討所提影像匹配法之應用性；特徵對象如池塘的轉折角、和道路交义口。結果顯示，線列與取樣像坐標之匹配精度，得以提昇0.2~0.4 個像元。

Usually, a standard least-squares image-matching functional model has radiometric shift and drift parameters, and geometric affinity parameters. This paper is focused to improve on a conventional stochastic modeling. Single-difference gray-levels are no longer dealt with to be independent and identically distributed. Scaling variance and covariance components are associated with some processed image segments. The estimated variance and covariance components are then used to form a new measurement covariance matrix, leading to iteratively adjusted weights until a steady parameter state is achieved. In theory, the proposed Blue-estimator is akin to the best invariant quadratic unbiased estimator. In practice, two Radarsat-1 synthetic aperture radar image scenes were made available to study an image-matching applicability to features such as an angular section of a pond and an intersection of roads. As a result, both the line and sample coordinates can be determined more accurately.