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系統識別號 U0026-1508201218041800
論文名稱(中文) 整合式資訊重建應用於衛星影像雲遮蔽之移除
論文名稱(英文) Cloud Removal from Satellite Images Using Integrated Information Reconstruction
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系碩博士班
系所名稱(英) Department of Geomatics
學年度 100
學期 2
出版年 101
研究生(中文) 賴鋼樺
研究生(英文) Kang-Hua Lai
學號 p66991107
學位類別 碩士
語文別 英文
論文頁數 57頁
口試委員 指導教授-林昭宏
口試委員-黃倬英
口試委員-蔡富安
口試委員-朱宏杰
中文關鍵字 雲遮蔽移除  影像資訊重建  最佳鑲嵌線  帕森方程式 
英文關鍵字 Cloud Removal  Image Reconstruction  Optimal Seam  Poisson Equation 
學科別分類
中文摘要 雲層遮蔽是光學式衛星影像上不可避免的問題,此問題限制了衛星影像的使用範疇以及增加後續資料分析的困難度,為利於後續應用的拓展,如何重建遮蔽區域下的地表資訊便成為一重要的研究議題。本研究為了拓展衛星影像之後續應用,致力於移除衛星影像上之雲層遮蔽並重建遮蔽區域下之地表資訊,藉此提升衛星影像的可用性。針對此問題,本研究提出一新的解決方法,利用衛星影像的週期性,也就是多時期影像資訊,來做為資訊重建的參考來源,再以本研究所提出之方法準確且無痕地進行地表資訊重建。基於多時期影像上的高時空相關性,本研究提出了片狀式鑲嵌法演算法(patch-based algorithm),其結合了影像之時空特徵分割(spatiotemporal segmentation)以及最佳鑲嵌線(optimal seam)之選取來對遮蔽區進行資訊重建。在挑選重建資訊的來源上,本研究提出的影像之時空特徵分割依影像中擁有相似時空變化特徵的位置進行分群,各群別再依品質評估指標自多時期資訊中尋找出最佳的回填資訊來源,而後續的資訊重建則依據挑選出的最佳回填資訊來源來做為重建資訊之參考依據。此外,為了無痕且準確地進行資料重建,本研究採用解算帕森方程式(Poisson equation)來對資訊重建進行最佳化解算,與其他相關研究不同的是本研究不直接使用雲層遮罩之邊界作為鑲嵌線,而另尋一最佳鑲嵌線使之通過影像均調區來降低輻射值的不連續性,並作為帕森方程式穩定的邊界條件,從而達到更佳的無縫鑲嵌成果。總括而言,雲遮蔽區域會先經由時空特徵分割步驟切割成數塊片狀區域,並依據影像品質排序的成果各自挑選出最佳的填補資訊來源,再以計算選取所得的最佳鑲嵌線輔助解算帕森方程式,準確且無痕地進行資訊重建。本研究所使用的定量分析資料是使用Landsat-7 Enhanced Thematic Mapper Plus (ETM+)衛星影像,在最後實驗成果顯示,不論是在準確度或者視覺上,本研究所提出的方法都較其他相關研究來得優異,尤其在大面積之遮蔽區域與異質區更能顯現本研究成效。
英文摘要 Clouds in satellite images can be regarded as information for measuring cloud liquid water which is useful in meteorology and hydrology or regarded as contaminations that partially obstruct surface observation of landscapes. This study addresses the latter issue in which clouds obstruct land covers, thereby resulting in missing data for passive image sensors. Cloud covers are generally present in optical remote-sensing images, which limit the usage of images and increase difficulty of data analysis. Thus, information reconstruction of could-contaminated images generally plays an important step in preprocessing of image analysis. This study aims to propose a novel method to accurately and consistently reconstruct information of cloud-contaminated pixels in multitemporal remote-sensing images. Based on the concept of utilizing temporal correlation of multitemporal images, we propose a patch-based information reconstruction algorithm that spatiotemporally segments a sequence of images into several patches with similar temporal variation, and then clones information from cloud-free and high-similarity patches to their corresponding cloud-contaminated patches. In addition, a seam passing through homogenous regions is determined for a cloud-contaminated region in order to reduce radiometric inconsistency in information reconstruction. A cloud-contaminated region is segmented into several patches and their corresponding cloud-free patches are determined by a quality assessment index, and the multi-patch information cloning is solved by an optimization process with a determined seam. These processes enable the proposed method to accurately and consistently reconstruct missing information. Qualitative analyses on sequences of images acquired by the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor and a quantitative analysis on a simulated data with various cloud contamination conditions are conducted to evaluate the proposed method. The experimental results show a clear superiority of our method, in terms of radiometric accuracy and consistency, over related methods, especially for large clouds in a heterogeneous landscape.
論文目次 摘要 I
Abstract III
致謝 V
Catalog VI
List of Table VIII
List of Figure VIII
Chapter 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Main Contribution 4
1.3 Thesis Organization 5
Chapter 2 RELATED WORK 6
2.1 Inpainting-based reconstruction 6
2.2 Multispectral-based reconstruction 7
2.3 Multitemporal-based reconstruction 8
Chapter 3 OVERVIEW 11
3.1 Landsat-7 ETM+ Satellite Images 11
3.2 Review of Information Cloning Technique 13
3.3 System Workflow 16
Chapter 4 METHODOLOGY 19
4.1 Cloud and Cloud-Shadow Detection 19
4.2 Image Intensity Normalization 20
4.3 Spatiotemporal Segmentation 21
4.4 Image Quality Assessment 24
4.5 Seam Determination 26
4.5.1 Search Space Determination 28
4.5.2 Cost Map Generation 31
4.5.3 Optimal Seam Determination 32
4.6 Information Reconstruction 33
Chapter 5 EXPERIMENTAL RESULTS AND ANALYSIS 37
5.1 Parameter setting 39
5.2 Result of information reconstruction 42
5.3 Performance of the proposed approaches 46
5.4 Information reconstruction of simulated cloud-contaminated image 48
5.5 Information reconstruction performance of each band 50
Chapter 6 CONCLUSIONS AND FUTURE WORKS 53
References 55
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