以往利用遙測影像分類之自動判釋土地覆蓋作業，多以各光譜段中各類別輻射值之差異進行分類，但影像各像元間之空間相關性，亦有益於分類之資訊；隨著衛星影像空間解析度的提昇，其所擁有之空間相關性資訊預期將更高，且有助於分類成果精度的提昇。本研究採用之物件導向分類方法，是利用影像中呈現自然相鄰狀態特徵的方法，將影像依均調性、空間相關性…等因素，分割成不等的影像區塊再進行分類，故為一種結合空間相關資訊和光譜資訊的分類技術。其中之物件導向分類方法:包括ECHO、Definiens，與以逐像元方式進行分類之分類法：並與高斯最大似然法、倒傳遞類神經網路及支持向量機等，進行分類成果比較。研究中利用三幅不同土地覆蓋複雜度之SPOT-5融合影像(空間解像力2.5公尺*2.5公尺)，針對上述五種分類演算法進行測試。結果顯示自動分類法應用於較繁雜之土地覆蓋，若影像具明顯的紋理特徵，則物件導向方法可得到相對較佳的分類成果；而支持向量機分類法對於過於複雜的土地覆蓋類型，因無法模擬出最適超平面區的分類別，故未能於各案例中穩定保持其分類精度之優勢。

For the land cover classification with remote sensing images, the spectral differences among classes provide the vital information. Besides the spectral information, the spatial relation of pixels also carries useful information for classification. Along with the increase of spatial resolution for satellite images, the spatial information is expected to be more abundant than before. Object based classification utilizes the spatial segmentation procedure prior to the classification. This study investigates the performance of ECHO, Definiens, and compared with the maximum likelihood, back-propagation, and support vector machines, classification schemes. Three SPOT-5 images with different complexity are selected for the experiments. The spatial resolution of these two images is 2.5 m, which were produced through the fusion process. From the experiments, it is shown that object based classification schemes provide stable result, while the SVMs showed dependency of data set.