傳統像元式影像分類法 (Pixel-Based Image Classification) 利用光譜灰度值(Grey-Layer Value) 逐一對每個像元進行分類，忽略影像地物中其他空間特徵，物件導向影像分類法 (Object-Oriented Image Classification) 則是將鄰近像元依其空間關聯性，組成具空間特徵的影像物件 (Image Object)，再依影像物件的分割尺度 (Segmentation Scale) 及特徵值 (Eigenvalue) 進行影像分類。本研究旨在探討裸露地影像物件分割尺度，使用多尺度分割 (Multi-Resolution Segmentation) 係依據異質性指標 (Homogeneity Criterion) 對鄰近像元或物件進行合併，尺度 (Scale) 參數為像元合併時影像物件容許的上限，此指標尚有顏色 (color)、形狀 (Shape)、緊緻度 (Compactness) 與平滑度 (Smoothness) 四種參數。本研究嘗試使用高解析度福衛二號衛星影像對裸露地進行物件導向影像分類，分類方法使用同質性 (Homogeneity) 與熵 (Entropy) 指標透過標準最近鄰(Standard Nearest Neighbor) 演算法，將分類結果對照內政部國土測繪中心之土地使用分類圖，探討影像物件在不同分割尺度參數下對影像分類結果精度之影響。相較於像元式影像分類，物件導向影像分類方法可獲得符合空間關聯之影像分類，亦容易結合地理資訊系統建置之屬性資料，更可快速製作易判釋之主題圖。
Conventional Pixel-Based Image Classification methods use merely grey-level values to classify pixels but ignore other spatial characteristics of ground objects. Object-Oriented Image Classification method connects adjacent pixels according to their spatial relativity in order to build up image objects and then carry out image segmentation according to the segmentation scale and their eigenvalue. This study aims to discuss the segmentation scale of bare land image objects. Multi-Resolution Segmentation is taken as a main method to combine adjacent objects according to Homogeneity Criterion, and then classify those combined image objects. Scale parameters represent the maximum limit when pixels merge into image objects. There are four parameters: color, shape, compactness and smoothness. In this study, we use high-resolution Formosat-2 satellite images to execute image classification on bare land. The land-use map provided by the National Land Surveying and Mapping Center, Ministry of the Interior, is used to compare the accuracy of the classification outcome and to study the accuracy of the results of the effect of image classification under different segmentation scales. Compared to the Pixel-Based Image Classification method, the outcome of Object-Oriented Image Classification method takes spatial relations into account. In this way, the attribute data of GIS and the image classification result can be easily combined and quickly made into a thematic map.
