遙測影像資料如今已成功地應用於判識地表物質，進行地表分佈與變遷之研究。當遙測資料有多重來源時，為使其總體判識率能優於任一來源，宜須選取適切之融合機制，整合多重來源，達到截長補短之功效。整合的方法，除了資料直接融合之外，「決策融合」機制亦常廣為採用，最常見者，例如linear opinion pool (LOP) 及 logarithmic opinion pool (LOGP)。決策融合機制乃由各個來源決策所建立之一融合機制，主要是探討如何結合各個來源之事前機率，而首當其衝須面對的問題，乃結合權重之估測。許多研究藉由各來源之分類結果來給定權重，值得注意的是，這些給定權重的方法在分析過程中往往需要可觀之計算量。
　　為了減低計算量，讓權重之估測迅速而不失真，本研究提出一參數式權重估測方法。在資料為高斯分佈之假設前提下，同時分析「來源相關」與「特定來源下之類別相關」兩種資訊。另外，為了解決參數估測可能遇到的估測誤差問題，本研究提出一最佳化解法。
　　實驗結果顯示，本研究提出之方法可以有效減低計算量，而判識率仍能維持不墜，亦符合多重來源總體判識率高於單一來源之基本要求。

　　In multisource classification, the logarithmic opinion pool (LOGP) mechanism is a widely used approach to decision fusion for multiple sources. The key problem of the LOGP mechanism is how to estimate associated weights. A number of methods have been proposed for weight estimation. However, most of the methods are time consuming.
　　In order to reduce the computational load, a parametric weight estimation method is proposed in this study. Based on the Gaussian model assumption, the weight estimation method takes into account not only source-wise but also class-wise information. Besides, an optimization scheme for weight determination is presented.
　　Our experiments show that our parametric scheme for weight estimation gives comparable performance in accuracy and relatively promising results in speed, compared to previously proposed schemes.

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