隨著科技日益進步，遙測衛星影像(Remote sensing satellite image)的發展也更加即時且精確的監測地表環境或是提早預防不可避免的災害。由於多變的天氣好比大氣懸浮粒子(Particles)所構成的雲層或是霧(Haze)，因這些現象導致在衛星影像中呈現較低對比(Low contrast)且失去了地表上部分的資訊。因此本論文針對單張衛星影像提出一個有效的除霧(Dehazing)方法，用意在於增強影像的對比度且濾除被霧所覆蓋的區域，進而顯現出遺失掉的資訊。首先利用區間二型遞迴式模糊小腦模型(Interval Type-2 RFCMAC Model)估測影像的初始傳輸平面(Initial transmission map)。對於處理過程所產生之光暈(Halo)及顏色過飽和(Color over saturation)等問題，將依序採用雙邊濾波(Bilateral filter)及二次函數(Quadratic function)非線性轉換來精煉(Refine)初始傳輸平面。在大氣光(Atmospheric light)的估測，採用前1%最亮的區域做為大氣光的顏色向量。最後，將精煉過的傳輸平面與大氣光做為重建影像(Reconstruct image)的參數。實驗結果顯示本篇所提出之衛星影像除霧方法，在重建影像能見度細節與色彩對比都有不錯的效能。為了更進一步驗證所提出方法之效能，分別進行視覺評估(Visual assessment)及量化評估(Quantitative evaluation)之分析，並與相關學者所提出之方法進行比較。經由視覺評估及量化評估分析後，在視覺與數據分析上確實能得到很好的結果。

With advances in technology, the development of Remote Sensing Satellite Image has been real-time and accurate to monitor the environment of the surface or prevent the inevitable disaster earlier. Owing to the changeable weather is just like clouds or haze constituted by atmospheric particles, then this phenomenon cause the low contrast presented in satellite image and lose many information on the surface of the earth. Therefore, in this paper we propose an issue for dehazing to single satellite image, which is to enhance the contrast of image and filter the haze that cover the location, then the losing information will be back. First, we use Interval Type-2 RFCMAC Model to estimate the initial transmission map of the image. When facing the problems of halo and color over saturation, we adopt the bilateral filter and the quadratic function nonlinear transformation step by step to refine the initial transmission map. At the atmospheric light estimation, we adopt the first 1% brightest area as the color vector of atmospheric light. Finally, we take the refined transmission map and atmospheric light as the two parameters of reconstruct image. The experiment result shows that the method of satellite image dehazing has an effective results in visibility details and color contrast of reconstruction image. Furthermore, in order to prove the effective results, we take the visual assessment and quantitative evaluation respectively to compare with other authors. After visual assessment and quantitative evaluation, we get the better result in visual and data indeed.

摘要 i
Abstract iii
致謝 v
目錄 vii
圖目錄 ix
表目錄 xi
第一章 緒論 1
1.1 研究動機 1
1.2 文獻探討 3
1.3 章節介紹 6
第二章 影像除霧模型 7
第三章 所提出的衛星影像除霧方法 10
3.1 區間二型遞迴式模糊小腦控制器估測初始傳輸平面 11
3.1.1. 區間二型遞迴式模糊小腦控制器 17
3.1.2. 改良型人工蜂群演算法 23
3.1.2.1. 人工蜂群演算法 24
3.1.2.2. 具動態分群之人工蜂群演算法 28
3.2 精煉傳輸平面 35
3.2.1. 利用雙邊濾波解決光暈 35
3.2.2. 利用二次函數解決顏色過飽和 40
3.3 大氣光的估測 42
3.4 還原影像 46
第四章 實驗與討論 47
4.1 視覺評估 50
4.2 量化指標評估 61
第五章 結論與未來工作 64
參考文獻 65

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