過去影像判釋多採用監督式學習作為分類器搭配多光譜進行影像判釋研究,但監督式學習在資料收集上需要耗費相當多的人力、物力以及時間,且多光譜由於空間解析度與光譜數量較低,無法準確判斷出光譜相似的地表物件。固若能以非監督式學習搭配高光譜這樣雍有豐富的影像資訊進行分析,準確判釋出地表物件,則可以減少實地探勘的人力、物力以及時間。而高光譜雖然有豐富的影像資訊,但如何篩選出對影像判釋有用的光譜資訊是一項重要的課題。 本研究探討如何從高光譜影像中篩選出重要的光譜資訊,並以水稻田為主要判釋對像,搭配監督式學習線性判別分析與非監督式學習以密度為基礎的聚類演算法,且使用主成份分析法進行前處理做平行研究設計出了以下四種研究案例:(a)多光譜及高光譜搭配線性判別分析 (b)多光譜及高光譜搭配以密度為基礎的聚類演算法 (c)多光譜及高光譜以主成份分析做前處理搭配線性判別分析 (d) 多光譜及高光譜以主成份分析做前處理搭配以密度為基礎的聚類演算法,將判釋結果建立誤差矩陣表以及主題圖後互相比較。
In general, image classification from the past use of supervised learning classifier with a multi-spectral image are considered in this study. However, supervised learning in data collection request quite a lot of manpower, material and time. On the other hand, multi-spectral and spatial resolution due to low resolution, it cannot accurately determine the spectral similarity of surface objects. If the unsupervised learning with hyperspectral of image information is analyzed and accurately judge, the substituted solution can be adopt to reduce time spending. Therefore, there are a wealth of multi-spectral image information, but how to filter out image classification on hyperspectral imaging is an important issue. This study focused on how to select important spectral information from hyperspectral imaging. The paddy fields images considering with a supervised learning linear discriminant analysis and unsupervised learning density-based clustering algorithm. The principal component analysis is used as pre-processing for parallel study designed the following four case studies: (a) multi-spectral and hyper-spectral with linear discriminant analysis (b) with a multi-spectral and hyper-spectral density-based clustering algorithm (c) multi-spectral and hyper-spectral principal component analysis with a linear discriminant analysis (d) multi-spectral and hyper-spectral principal component with density-based clustering algorithm. The results are compared with each other after the error matrix and the theme maps are drawn.