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研究生:陳羿廷
研究生(外文):Chen, Yi-Ting
論文名稱:應用Sentinel-1A合成孔徑雷達時空資料於中台灣水稻辨識
論文名稱(外文):Application of Spatial-Temporal Sentinel-1A SAR Data for Central Taiwan Rice Discrimination
指導教授:張麗娜張麗娜引用關係
指導教授(外文):Chang, Lena
口試委員:張順雄張陽郎
口試委員(外文):Chang, S. -H.Chang, Yang-Lang
口試日期:2020-01-07
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:通訊與導航工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:63
中文關鍵詞:水稻生長特徵Sentinel-1A合成孔徑雷達時空資料
外文關鍵詞:rice growth characteristicsSentinel-1ASynthetic Aperture Radarspatial-temporal data
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水稻是台灣最重要的糧食作物,種植面積約占台灣總面積的5%,因此水稻偵測為一台灣農林觀測最重要的工作項目之一。合成孔徑雷達(Synthetic Aperture Radar, SAR)具備可全天候觀測及可穿透雲層特性,可解決光學衛星影像因天候影響而無法有效觀測的問題。Sentinel-1A為歐洲太空總署的地球觀測衛星,可以提供台灣地區VH與VV極化的SAR資料,利用此長期觀測SAR之多時序資料,可以了解地物在不同時期的變化。本研究將利用Sentinel-1A提供之SAR VH與VV極化的時空資料,分析台灣中部雲林與彰化水稻種植的範圍。相較於傳統方法只使用時序資料中最小值與最大值,本研究將依據水稻生長週期中完整時序資料,建立水稻生長曲線模型,藉此萃取水稻生長特徵,包含SAR後向散射最大值(Maximum value of Backscatter coefficient Index, MBI)、生長高度差值特徵(Difference between maximum and minimum values of Backscatter Index, DBI)、生長時間特徵(Time Interval Index between rice tillering and maturity stages, TII)、水稻抽穗期生長速率特徵(Grow Rate Index, GRI)及生長趨勢形狀特徵(Growthing trend Shape Index, GSI)。透過多種特徵,並選取適當分類閥值,辨識水稻區域,能夠有效地提升分類總體精度。本研究針對2016和2017年,Sentinel-1A升軌和降軌模式及VH與VV的極化方式,共蒐集8組實驗資料,進行中台灣水稻範圍偵測。實驗結果顯示,相同極化下,使用升軌或降軌模式,並不會影響辨識結果,而相同軌道下,因VV極化反射較弱,造成後向散射變化不明顯,因此VH極化分類結果較VV極化結果要好。實驗結果也顯示相較於傳統僅使用生長高度差異之特徵,本研究所提結合多種水稻生長特徵分類法,可提升水稻辨識總體精度約7%。研究所提方法在VH極化下,水稻偵測總體精度平均可達90%以上,而在VV極化下,水稻偵測總體精度約為80%。
Rice is the most important food source in Taiwan and the planting area of rice is about 5% of Taiwan’s land area. Therefore, accurate detection of rice planting area is essential for Taiwan’s agricultural development. The Synthetic Aperture Radar (SAR) has shown to be very useful in environment monitoring due to its high resolution and efficient ability in penetrating clouds even under unfavorable weather conditions, day and night. Sentinel-1A is an Earth observation satellite of the European Space Agency, which can provide SAR data of VH and VV polarization for Taiwan. We can understand the changes of crop objects in different periods by using the long-term observation of SAR data. This study proposed a feature-based decision approach to describe the mapping of rice cultivation in Yunlin and Changhua, central of Taiwan, by using the spatial and temporal SAR data with VH and VV polarization provided by Sentinel-1A. Instead of using the maximum and minimum values of backscatter coefficient in SAR data, as most studies in the literature, this study first built a rice growth trend model based on complete time series data in the rice growth cycle. Then, from the developed model, five characteristics of rice growing are extracted, including Maximum value of Backscatter coefficient Index (MBI), Difference between maximum and minimum values of Backscatter Index (DBI), Time Interval Index between rice tillering and maturity stages (TII), Growthing Rate Index (GRI) and Growthing trend Shape Index (GSI). A decision method based on the combination of five extracted features was proposed for rice planting mapping to improve the rice classification accuracy. In this study, we collected 8 sets of experiment data from Sentinel-1A with ascending and descending orbit modes, VH and VV polarization, in 2016 and 2017, respectively, in the field of central Taiwan. Experiment results show that the proposed method can achieve more than 90% overall accuracy in rice classification under VH polarization. Whereas, the overall accuracy of rice classification is about 80% for VV polarization. These results validate that rice classification using VH polarization data is better than VV polarization data. Also, the ascending or descending orbit mode with the same polarization will not affect the classification results. Furthermore, comparing with the traditional method that uses one feature, DBI, the proposed method can improve the overall accuracy of rice discrimination about 7%.
摘要 III
Abstract IV
目錄 V
圖目錄 VII
表目錄 IX
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 論文架構 3
第二章 文獻回顧 4
2.1 SAR影像相關介紹 4
2.2 SAR影像應用於農作物分類相關研究背景 5
2.3 SAR影像水稻分類研究 8
第三章 研究資料簡介 10
3.1 適用於水稻萃取之SAR影像資料 10
3.2 Sentinel-1A影像 12
3.3 研究資料來源介紹 13
3.3.1 水稻分布資料來源 15
3.3.2 台灣地區SAR資料來源 16
第四章 研究方法 18
4.1 SAR影像前處理 18
4.1.1 輻射校正 19
4.1.2 地形校正 21
4.1.3 雜訊濾波 23
4.1.4 地真套疊 27
4.2 時序向量處理及影像切割 29
4.2.1 時空平滑處理 29
4.2.2 多維度SAR影像切割 35
4.3 水稻生長模型建置與特徵萃取 36
4.3.1 建立生長模型 37
4.3.2 特徵萃取 38
第五章 實驗結果與討論 45
5.1 升軌模式之水稻分類結果與評估 45
5.1.1 2016年VH與VV極化特徵閥值與訓練資料 45
5.1.2 2016年VH與VV極化分類結果 48
5.2 降軌模式於水稻分類結果與評估 51
5.2.1 2016年VH與VV極化特徵閥值與訓練資料 51
5.2.2 2016年VH與VV極化分類結果 52
5.3 水稻分類結果討論 55
5.3.1 軌道模式於水稻辨識影響 55
5.3.2 極化方式於水稻辨識影響 55
第六章 結論與建議 61
6.1 結論 61
6.2 建議 61
參考文獻 62
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