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系統識別號 U0026-0509201617413500
論文名稱(中文) Landsat 8衛星影像支持向量機雲偵測演算法
論文名稱(英文) Cloud Detection Based on Support Vector Machine for Landsat 8 Imagery
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 104
學期 2
出版年 105
研究生(中文) 李冠毅
研究生(英文) Kuan-Yi Lee
學號 P66031038
學位類別 碩士
語文別 英文
論文頁數 51頁
口試委員 口試委員-張智安
口試委員-蔡富安
指導教授-林昭宏
中文關鍵字 雲偵測  分類  支持向量機 
英文關鍵字 Cloud Detection  Classification  Support Vector Machine 
學科別分類
中文摘要 光學遙感探測衛星影像中普遍有雲覆蓋地表的問題,此問題限制了影像的使用且增加處理影像與資料分析的難度,許多衛星影像相關研究,如影像拼接、大氣效應校正、植生指標分析、地表物分類和地表變遷分析皆需了解雲的位置後再做後續之處理,因此,雲偵測法為許多研究前處理的過程之一。在先前研究中,門檻值法是最普遍與最有計算及執行效率的方法,然而,其設定的門檻值通常只適用於該研究地區或某特定環境,地球環境則會因為時間不同而不斷推移變化,如果在不同時間與地點所拍攝的影像卻使用同一組門檻值,勢必會有錯誤偵測的情況發生。此外,門檻值演算法也會因為環境改變而出現例外的情況,而例外情況的漸增會使得演算法的實用性遞減。因此本研究捨棄了門檻值的使用,利用支持向量機輔助雲偵測演算法可避免使用門檻值演算法所出現之相關問題,且本研究以統計模式建立分類基準來避免相對主觀的門檻值。
本研究之關鍵在於支持向量機演算法中所使用的分類特徵。Landsat 7之自動雲偵測演算法(ACCA)依照不同目標物的物理特性來區分雲和其他物體;同理,另一演算法Fmask也使用各種門檻值和機制找出(來區分)雲、雲陰影、雪和水體等等;本研究則綜合兩者之機制產生用於支持向量機之特徵。另外,影像分類的過程中,空間和紋理資訊皆應被利用,因此,本研究紋理特徵部分使用Hotelling transform再經過共生矩陣產生之紋理影像,我們的目標是將影像分類為四大類:雲、雪、水及其他。實驗過程中使用的影像為Landsat 8之影像,其感測器包含作業地表成像儀(OLI)和熱紅外感測器(TIRS),實驗地區則含蓋農地、雪地和海島,實驗結果顯示本研究所提出之方法其整體精度介於93%至97%且高於其他相關研究。
英文摘要 Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation indices, land cover classification, and land cover change detection. Therefore, the generation of cloud mask is one of important pre-processing steps in many remote-sensing researches. In previous studies, thresholding is a common and efficient method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there many exceptions have to control, and the Earth environment and atmosphere changed dynamically. Using a set of threshold values on various image data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study.
The features used in a classifier is the key to a successful classification. In the popular thresholding-based approach, called Automatic Cloud Cover Assessment (ACCA), the cloud is distinguish from other objects based on the physical characteristics of cloud and other targets on the ground. Similarly, the algorithm called Fmask adopted a lot of thresholds and criteria to screen clouds, water, and snow. Following these two algorithms, the spectral features used in the proposed method is defined by the ACCA and Fmask algorithms. Spatial information is also important in the classification processing in addition to the spectral information. Consequently, co-occurrence matrix of the Hotelling transform is used in proposed method to extract the spatial or called texture features. In this study, images are partitioned into four groups: cloud, snow, water and the others. In experiments, images acquired by the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the overall detection accuracy of the proposed method is about 93% to 97%, which is better than thresholding-based methods with the default threshold values and is comparable to that with the tuned threshold values.
論文目次 摘要 I
Abstract III
致謝 V
CATALOG VI
List of Table VII
List of Figure VII
Chapter 1 Introduction 1
Chapter 2 Background 7
2.1 Review of Landsat 7 automatic cloud cover assessment 7
2.2 Review of Fmask algorithm 10
Chapter 3 Methodology 11
3.1 SVM classification 12
3.2 Spectral features 14
3.3 Texture features 26
Chapter 4 Experiment Results and Discussions 29
4.1 Study data 29
4.2 Experimental Results 31
4.3 Evaluation 38
Chapter 5 Conclusions and Future works 45
References 48
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