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系統識別號 U0026-2708201418491800
論文名稱(中文) 多時期光學式衛星影像輻射同態化及雲偵測演算法使用不變像元資訊
論文名稱(英文) Radiometric Normalization and Cloud Detection of Multi-Temporal Optical Satellite Images Using Invariant Pixels
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 102
學期 2
出版年 103
研究生(中文) 林柏毅
研究生(英文) Bo-Yi Lin
學號 P66014094
學位類別 碩士
語文別 英文
論文頁數 58頁
口試委員 指導教授-林昭宏
口試委員-王驥魁
口試委員-張智安
口試委員-蔡榮得
中文關鍵字 光學式衛星影像雲偵測  擬恆定特徵物  影像輻射同態化 
英文關鍵字 cloud detection  pseudo-invariant feature  radiometric normalization 
學科別分類
中文摘要 「雲」在光學式衛星影像中可被當成大氣中水量的量測資訊,也可被當成遮蔽地表的物質。因此,透過雲偵測演算法區分影像中有雲及無雲的像元在遙測領域中是一項必要且重要的處理程序。當影像經過了輻射同態化的處理之後,多數先前的研究採用多時期及多波譜的資訊,相較於一張無雲的參考影像,建立多個以門檻值為基礎的濾波器進行雲偵測。雖然這個策略能有效率且準確的分辨雲與非雲像元,但其成果好壞仍主要仰賴於影像輻射同態化是否順利,以及挑選之無雲參考影像的品質。在輻射同態化中,最主要的困難在於處理影像有雲覆蓋的問題。雲覆蓋會影響同態化的穩定度與正確度,但現有的雲偵測演算法對於輻射同態化卻又非常敏感。因此本研究提出使用加權不變像元進行輻射同態化及雲偵測,利用時序上的相關性,從一對有雲影像中繪製散點圖之後,透過加權主成份分析取出一組不變像元。這些像元在不同時期,其灰階值變化大致上會呈現線性關係。透過品質管理機制,可挑選適當的不變像元,或稱為「擬恆定特徵物」進行輻射同態化。這些挑選出的不變像元,還能利用本文所提的加權方法,針對每一張有雲影像產生一張專屬的無雲參考影像,並用此無雲參考影像輔助雲偵測。本研究所使用的測試資料為Landsat-7 Enhanced Thematic Mapper Plus (ETM+) 衛星影像,所挑選的影像具各種雲的型態及各式地表特徵。實驗結果顯示,本文方法在影像輻射同態化方面有能力處理具有雲覆蓋影像。且透過不變像元產生之無雲參考影像,也能讓雲偵測的準確率從97.25%提昇至99.08%。
英文摘要 Clouds in optical satellite images can be regarded as information for liquid water measurement or as contaminations that obstruct landscape observation. Thus, cloud detection that discriminates cloud and clear-sky pixels in images is a necessary processing step in remote sensing applications. With radiometric correction/normalization preprocessing, most previous studies utilized temporal and spectral information to develop thresholding-based filters with the aid of a cloud-free reference image. Although this strategy can efficiently and accurately identify cloud pixels, detection accuracy mainly relies on the success of radiometric correction/normalization and quality of the selected cloud-free reference image. Radiometric normalization generally suffers from cloud covers; cloud detection is sensitive to radiometric normalization. In this study, a method based on weighted invariant pixels is proposed for radiometric normalization and cloud detection. Utilizing temporal correlations, a set of invariant pixels obtained from the scatterplot of two adjacent images in time series through weighted principal component analysis are extracted from a time series of cloud-contaminated images with the reason that the variations of image digital counts during a period are linear. The image is normalized by using the selected invariant pixels with quality control or the so-called pseudo-invariant features. In addition, a composed cloud-free reference image is generated for each cloud-contaminated image by using the selected invariant pixels with a proposed weighting scheme. In the experiments, qualitative analyses of image sequences acquired by Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor and quantitative analyses of image sequences with various cloud contamination conditions and landscapes are conducted to evaluate the proposed method. The experimental results show that the proposed radiometric normalization has the ability to deal with images that contain various clouds. Moreover, cloud detection accuracy is improved by 0.14% to 4.40% with the use of the generated reference images with a thresholding-based detection method.
論文目次 摘要 I
Abstract III
致謝 V
List of Table VII
List of Figure VII
Chapter 1 Introduction 1
Chapter 2 Background 6
2.1 Review of radiometric normalization 6
2.2 Review of multi-temporal cloud detection method 8
Chapter 3 Methodology 10
3.1 System Workflow 10
3.2 Invariant pixel determination and radiometric normalization 12
3.3 Reference image generation and cloud detection 18
Chapter 4 Experimental Results and Discussion 22
4.1 Landsat data 22
4.2 Experimental Results 23
4.3 Evaluation 29
Chapter 5 Conclusions and limitation 41
References 43
Appendix – All experimental results 46
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