隨著科技的進步與衛星的多元化，影像套合在遙感探測之影像處理方面是一個非常基本的應用，後續應用包括了影像融合、變遷偵測等。因此，本研究將利用尺度不變特徵轉換、Canny特徵萃取、最小二乘匹配法等技術，進行多來源遙測影像匹配與套合。首先利用人工點選或是利用尺度不變特徵轉換(SIFT)運算元，尋找主影像與附屬影像之間的初始對應關係，接著分別利用Canny運算元萃取兩張影像的邊緣，並且清理較破碎的邊緣後，利用成本函數之梯度方位與距離進行邊緣點匹配。之後再以匹配成功的初始共軛點為中心開啓影像窗，進行影像分塊與輻射參數校正，主影像窗與校正後之附屬影像窗以最小二乘匹配法進行更精確的定位。最後將所有共軛點對利用薄板樣條法(TPS)，並結合粗差濾除後建立套合影像，各項實驗結果證明上述方法能順利完成不同資源衛星影像之套合任務。

Image registration is a key issue in many image processing applications in remote sensing. Examples of these applications include change detection using multiple images acquired at different times, and fusion of image data from multiple sensor types.SIFT (Scale Invariant Feature Transform), Canny feature matching and least-squares area matching method are proposed in this research. At first, the initial matching point pairs are detected from manual adjustment or the SIFT algorithm, which is invariant to image scale. And then, edges in both images are located by using the Canny algorithm and broken contours are cleaned. Furthermore, more matching point pairs are selected using a cost function that measures the gradient orientation and distance between all possible pairs of the points. Pairing image windows are built and segmented to get radiometric parameters, and the radiometric parameters are used here to modulate the slave image window. Finally, master image window and modulated slave image window are matched by least-squares matching, and conjugate points are found. The Thin-Plate Splines (TPS) and blunder removal methods are used to register master and slave images. Experimental results show that numerous matching points can be obtained correctly and automatically, and different satellite images can be registered precisely.