伴隨近年測量技術及感測設備發展迅速，衛星影像引起大量關注並廣泛使用於多項應用範疇，例如：土地利用偵測、環境變遷偵測與軍事監控等。然而衛星影像常出現雲層視覺遮蔽情形，尤以氣候潮濕的熱帶地區雲量遮蔽問題越發嚴重，限制了衛星影像的使用亦增加影像分析之困難。本論文將提出一衛星影像雲影區域之色彩補償演算法，期許減少因雲影覆蓋而造成之影響。在此方法中，陰影區域之像素強度以統計方法與全球性優化過程進行補償處理。其色彩補償將透過帕森方程式於梯度影像中尋求邊界最佳解，通過色彩調整和帕森方程式求解的過程，可進一步獲得被偵測到陰影區域之補償影像梯度及經色彩補償的影像成果。本研究針對美國地質調查局(U.S. Geological Survey，簡稱USGS) 網站所下載之Landsat-7衛星影像進行實驗分析，實驗結果顯示本法可以有效增顯補償衛星影像中的陰影像素，並以視覺成果檢驗一模擬資料雲影補償之成效。

With the development of sensor equipment and surveying techniques, satellite images have drawn considerable attention. These images have been widely used in numerous applications, including land-use detection, environment change detection, and military monitoring. However, cloud covers are generally present in optical satellite images, particularly in humid tropical areas, thereby limiting the usage of optical images and increasing the difficulty of image analysis.
In this thesis, a cloud shadow compensation approach aimed at easing the difficulties attributed to cloud covers is introduced. In the proposed approach, the intensities of shadow pixels are compensated by a statistic adjustment method and a global optimization process. The problem of intensity compensation is formulated as a Poisson equation and then solved on the gradient domain. Through color adjustment and Poisson equation solving, a compensated gradient field, as well as a compensated color field, can be obtained for the detected shadow regions.
Experimental analyses on the satellite images acquired by the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor were conducted. The experimental results show that the proposed approach can successfully compensate for the intensities of shadow pixels. The evaluation of the proposed approach and the quantitative and qualitative analyses on simulated data were conducted via quality index and visual inspection, respectively.

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