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系統識別號 U0026-2701201503582900
論文名稱(中文) 混合式資訊重建演算法應用於多時期Landsat衛星影像修復
論文名稱(英文) A Hybrid Information Reconstruction Algorithm for Multitemporal Landsat Image
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 103
學期 1
出版年 104
研究生(中文) 陳誌彬
研究生(英文) Zhi-Bin Chen
學號 P66011070
學位類別 碩士
語文別 英文
論文頁數 74頁
口試委員 指導教授-林昭宏
口試委員-饒見有
口試委員-李政軒
中文關鍵字 Landsat衛星影像  雲遮蔽移除  資訊重建  帕森方程式 
英文關鍵字 Landsat ETM+  Cloud Removal  Information reconstruction  Poisson Equation 
學科別分類
中文摘要 雲遮蔽是被動式衛星影像無法避免的問題,然而在多時期衛星影像中,雲遮蔽區不盡相同,因此可以從其它時期無雲遮蔽影像擷取區塊資訊,並將擷取的資訊鑲嵌回填回雲遮蔽區域,輔以變遷分析修正填補資訊,以達到兼具視覺自然與數值可靠的重建成果。目前已有許多修補雲遮蔽影像的方式,主要可分成區塊式填補(patch-based)及逐像元填補(pixel-based)兩種方法,其中,區塊式填補從其它時期影像擷取非雲遮蔽區塊資訊,利用影像鑲嵌技術將非雲遮蔽區塊回填於遮蔽區,如此可以得到較有具幾何連續性的成果,使成果達到視覺自然的效果;而逐像元填補則是逐一針對各像元進行時序或空間的變遷分析與預測,填補過程中各像元獨立計算,成果較可靠,適用於數據分析應用,但幾何連續性較低,成果容易含有雜訊而不自然。本研究提出結合上述兩種方式的填補方法,逐像元預先填補一部分點位,並將這些高可靠性的點位加入區塊式填補的約制條件中,如此提升區塊式填補的可靠性,使成果兼具可靠性與視覺自然。研究方法主要分兩階段,第一階段為逐像元填補,由於相似地物在各時間點具相似反射光譜,隨時間的光譜變化量亦相似,雲遮蔽區內各像元有機率在非雲遮蔽區找到與其為相似地物的像元,利用這些相似像元,即可反推一部分雲遮蔽像元的反射光譜及光譜變化量。第一階段中,僅少數雲遮蔽像元能找到足夠的相似像元,因此,第一階段僅少數雲遮蔽像元能被填補,稱為固定點,其雖然量少,但可靠性極高;而第二階段為區塊式填補,利用帕森影像處理法進行影像鑲嵌,在其它時期擷取非雲遮蔽區塊資訊,並回填於遮蔽區,此影像處理法能同時調整色調並消除鑲嵌線,將填補資訊轉換成帕森方程式(Poisson equation)並求解最佳解以進行資訊重建,求解過程中,加入第一階段所求得的固定點做為約制條件以提升填補結果的可靠性,並有效抑制由填補邊界至中央的誤差傳播。本研究所使用的實驗資料是Landsat 7 Enhanced Thematic Mapper Plus (ETM+)衛星影像,為了評估實驗結果,實驗中使用的雲遮蔽影像是由非雲遮蔽影像模擬而成,最後的實驗成果顯示,本研究所提出的方法確實可以正確的重建雲遮蔽區塊,且比其它研究方法有更好的準確度與視覺自然。
英文摘要 The key to information reconstruction of cloud-contaminated satellite images is to recover missing data by utilizing temporal and contextual information while maintaining radiometric accuracy and consistency. Most previous studies achieved this objective by using patch-based information cloning or pixel-based contextual prediction. Patch-based methods that utilize temporal correlation of multitemporal images have the advantage of radiometric consistency, whereas pixel-based methods that use spatial contextual information can achieve radiometric accuracy. A hybrid method that integrates patch-based cloning with pixel-based prediction is proposed to provide a radiometric accurate and consistent reconstruction. In the proposed method, a small set of cloud-contaminated pixels with high-confidence filling results is determined on the basis of the fact that same-class pixels have similar spectral characteristics and exhibit similar temporal changes between dates. These pixels, which are called fixed points, are used to optimize patch-based radiometric cloning. Radiometric patch cloning is mathematically formulated as a Poisson equation and solved by using an optimization process. Several cloud-free and high-similarity patches are optimally cloned to a corresponding cloud-contaminated region under constraints from fixed pixels. Cloning optimization can lead to radiometric consistent results. Fixed-point constraints can improve radiometric accuracy by reducing error propagation in radiometric cloning. In experiments, simulated images and actual image sequences acquired by Landsat Enhanced Thematic Mapper Plus sensor are used to assess the performance of the proposed hybrid method. Experimental results indicate that our method can accurately recover the value of cloud-contaminated pixels, and the reconstruction accuracy is improved in comparison with related methods.
論文目次 摘要 I
Abstract III
CATALOG VI
List of Table VIII
List of Figure VIII
Chapter 1 Introduction 1
Chapter 2 Related work 4
Chapter 3 Methodology 9
3.1 Method workflow 9
3.2 Cloud detection 11
3.3 Radiometric normalization 13
3.4 Fixed point determination 18
3.4.1 Pixel-based information reconstruction method 18
3.4.2 Fixed-point addition 29
3.5 Patch-based information reconstruction with fixed points 34
3.5.1 Poisson information reconstruction 34
3.5.2 Information reconstruction with multi-source and fixed point 37
Chapter 4 Experimental Results and Analysis 43
4.1 Study areas 43
4.2 Parameter setting 44
4.3 Method evaluation 50
4.3.1 Performance of proposed approach 50
4.3.2 Evaluation of fixed point position 57
4.4 Experimental results 61
Chapter 5 Conclusions and future works 69
Reference 71
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