隨著衛星影像解析度的提昇，使我們在衛星影像上可看到的資訊及物體愈來愈多也更加豐富，運用也更廣；世界各國及學術研究機構均積極研發，各國廠商也參與研究搶食這塊大餅，創造許多影像處理技術，但衛星影像拍攝後需要經過一些修正及加值處理，這樣影像產品才能運用的更廣。 衛星影像因拍攝時間、角度及衛星感測器的不同，導致影像會有尺度大小不一、旋轉角度不同及偏移等現象，如何識別影像中的物體，物體特徵萃取技術也顯的更重要了；本文主要運用SIFT特徵運算法來匹配不同感測器、偵照日期及角度之衛星影像，進一步達到影像自動匹配，結果顯示SIFT特徵可以有效解決不同時期、不同感測器衛星影像間之匹配問題。 ;With finer resolution of satellite imagery, people can extract abundant information and develop more applications from it. Nowadays, research institutes and commercial imagery companies all over the world work intensively to develop many image processing techniques. However, satellite imagery still requires correction and value-added processing for further utilization and applications. Because of the difference for imagery collection time, angle and sensors, images at the same location still have different scale, rotation and translation. In such case, feature extraction is the key technique for target identification in different images. In the thesis, we try to use Scale Invariant Feature Transform（SIFT）to extract features and match them in images with different collection conditions. The result shows that SIFT is capable of extracting stable features, and many of them are matched even the images have different scale and distortion.
