超光譜影像感测器提供影像中每一個像元許多且連續的光譜波段資料，並使其有可能放每一個像元内提供不同地物與種類的訊息。本文中以監督式分類的交互相關法與光譜角製圈法對AVIRIS超光譜影像中四種不同地物光譜特徵進行匹配；此兩種方法所獲得的成果相似，其中水體與草地的匹配分顺成果很好；道路與建物部份亦有不错的成果，唯此可再進一步的處理（如先去除混合等）以提高其分類精度。

Hyperspectral sensors can provide each pixel with many continuous spectrum data, and make it possible for each pixel to give us information in different features and classes. In this article, two supervised classification methods, Cross-Correlation and Spectral Angle Mapper, are used for matching with four different features in AVIRIS image. The results from those two methods are similar. Among other things, the results of matching water and grass were good; the results of matching roads and buildings were fair, but further process (i.e. unmixing) may increase classification accuracy.