空間特徵分類器支援向量機之研究

Space Characteristic Classifier of Support Vector Machine for Satellite Image Classification

雷祖強(Tsu-Chiang Lei) ； 周天穎(Tine-Yin Chou) ； 萬絢(Shiuan Wan) ； 楊龍士(Long-She Yang) ； 許晉嘉(Jin-Jia Syu)

《航測及遙測學刊》 12卷2期 (2007/06) Pp. 145-163

https://doi.org/10.6574/JPRS.2007.12(2).4

摘要

如何選用適當的分類器一直是影像處理問題中經常被討論的一個研究重點，然而隨著衛星影像資料複雜度與資料量的增加，傳統線性分類器（例如：最大概似法、最短距離法等）已經無法達到有效分離類別之目標，因此本研究利用資料挖掘理論當中的－支援向量機法(Support Vector Machine, SVM)，來做為探討遙測影像分類研究之新課題。本研究選擇了高解析度QuickBird衛星影像及紋理資訊(Texture Information)做為影像分類時之資料來源，並利用最大概似法與支援向量機法來達到分類的目的。研究成果顯示，影像透過多組紋理並進行分類後之成果，整體來說，是支援向量機的分類精度優於最大概似法，精準度值較高也較穩定，不會像最大概似法有高低震盪的情形發生。而且就影像個別類別區塊化的能力來說，也是以支援向量機的成果較佳，特別是在「水稻」這個類別上面。因此本研究特別發現以支援向量機分類方法處理加入紋理資訊的影像，整體精度將會是優於傳統最大概似分類法之結論。

關鍵字

影像分類 ； 紋理資訊 ； 支援向量機

並列摘要

It is of considerable interest to find an optimal classifier that has been discussed in the field of spatial information. In essence, there are many image classification methods, e.g. Maximum likelihood (MLH), K-nearest…. However, most of the linear classifiers are not capable of handling the complexity and the huge amount of the very high resolution image data. Thus, Support Vector Machine (SVM) is one of the powerful non-linear data mining classifier which is adopting to resolve the classification problems in this study. The high resolution QuickBird satellite images with additional texture information are the study material. The MLH method is used as a parallel study for the comparison on overall accuracy. The contribution of this study found that the overall accuracy of SVM is stable than that of MLH. More specifically, the overall accuracy of SVM is 87.3% (Kappa=0.8416) which is apparently higher than that of MLH (overall accuracy of SVM is 83.73% with Kappa= 0.7994). On the other hand, SVM can display better classification outcomes in the image pattern of ”paddy rice” than that of MLH. In fact, the additional texture information can deal with noise effectively. The study find out that SVM can potentially perform higher image classification ability than the conventional MLH method.

並列關鍵字

Image Classification ； Texture Information and Support Vector Machine