Landsat 8衛星影像支持向量機雲偵測演算法

Cloud Detection Based on Support Vector Machine for Landsat 8 Imagery

李冠毅(Kuan-Yi Lee) ； 林昭宏(Chao-Hung Lin)

《航測及遙測學刊》 22卷4期 (2017/12) Pp. 227-241

https://doi.org/10.6574/JPRS.2017.22(4).1

摘要

光學遙感探測衛星影像中普遍有雲覆蓋地表的問題，同時限制處理影像的方法。多數先前研究中，使用門檻值是最普遍的方法，然而，因地制宜的門檻值通常只適合該研究地區；地球環境不斷推移變化，如持續用同一門檻值，勢必有失效情況發生。根據支持向量機雲偵測演算法，可避免上述問題。故本研究利用統計模式建立分類基準，避免使用相對主觀的門檻值，依照雲和其他目標物對於不同波段之物理特性相互調整出合適光譜特徵；紋理特徵部分則使用Hotelling transform再經過共現矩陣產生之紋理影像。經實驗後，結果顯示本研究提出之方法其整體準確度介於93%至97%。

關鍵字

雲偵測 ； 分類 ； 支持向量機

並列摘要

Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images. In previous studies, thresholding is a common and rapid method in cloud detection. However, a selected threshold is usually suitable for local study areas, and it may be failed in other cases. Besides, there are many exceptions to control, and the environment is changed dynamically. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method. According to the physical characteristics of clouds and other objects, the spectral features are appropriately designed for classification. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix of the Hotelling transform is used in proposed method. Experiment results demonstrate the detection accuracy of the proposed method is about 93% to 97%.

並列關鍵字

Cloud Detection ； Classification ； Support Vector Machine