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研究生:林俊雄
研究生(外文):Chun-Hsiung Lin
論文名稱:結合QB光譜及組織特徵於樹種分類
論文名稱(外文):Integrate QB Satellite Spectral and Texture Features to Tree Species Classification
指導教授:林金樹林金樹引用關係
指導教授(外文):Chinsu Lin
學位類別:碩士
校院名稱:國立嘉義大學
系所名稱:森林暨自然資源研究所
學門:農業科學學門
學類:林業學類
論文種類:學術論文
論文出版年:2004
畢業學年度:94
語文別:中文
論文頁數:101
中文關鍵詞:捷鳥衛星影像光譜特徵組織特徵最適視窗大小
外文關鍵詞:QB satellite imagespectral characteristictexture characteristicoptimal window size
相關次數:
  • 被引用被引用:4
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  • 評分評分:
  • 下載下載:33
  • 收藏至我的研究室書目清單書目收藏:1
理論上,不同種類的植物擁有不同的光譜特徵,前人研究報告主要以土地利用型之分類為重點,較少的研究是討論樹種間光譜特徵變化,而用遙測影像判釋植生種類,因有許多的限制因子影響了樹種判釋準確度,故較分辨土地利用型困難。本研究採用捷鳥(QuickBird)衛星影像為研究材料,因其為高解析力影像,可讓像元較為單純進而使樹木冠層影像單純化,增加樹木冠層像元資料之可用價值。
本研究的目的在於以衛星影像來分辨樹種,用QB全色態及多光譜影像採用了灰階共現矩陣(GLCM)組織特徵萃取、植生指標、Gamma、Lee-sigma及Mean組織濾波器等方法,將原本5波段之QB影像延展為18波段的影像,以資料延展的概念提升樹種分辦能力,而因延展後18波段影像做樹種分類時,會有Hughes現象的發生,因此利用主軸轉換法來避免之。
QB多光譜影像分類結果全區分類準確度(OA)值60.61%, 值0.5915,區外分類準確度9.18%;GLCM組織特徵萃取以MEAN+HOM +CON+DIS+ENT的分類結果會最佳,OA值29.61%, 值0.2750,區外分類準確度3.83%;植生指標以ARVI+EVI+NDVI+SAVI+VARI的分類結果最佳,OA值50.86%及 值0.4922,區外分類準確度7.72%;Gamma濾波器以33*33視窗大小處理之效果最佳,OA值98.66%及 值0.9859,區外分類準確度8.98%;Lee-sigma濾波器以39*39視窗大小處理之效果最佳,OA值98.06%及 值0.9797,區外分類準確度6.93%;Mean濾波器以39*39視窗大小處理之效果最佳,OA值98.95%及 值0.9890,區外分類準確度10.38%。
訓練樣本大小及濾波器處理視窗大小關係的研究中,以Gamma濾波器處理之結果,經平均計算後得到最適視窗大小約為樹種訓練樣本長的1.78±0.80倍,寬的1.81±0.84倍;以Lee-sigma濾波器處理之結果,約為樹種訓練樣本長的2.03±1.10倍,寬的2.07±1.19倍;以Mean濾波器處理之結果,約為樹種訓練樣本長的1.62±0.82倍,寬的1.68±1.00倍,因標準偏差過大,顯示訓練樣本與最適視窗大小並無明顯的關係存在。而以三種濾波器處理得到之各樹種最適視窗做比較, Gamma vs. Lee-sigma之相關係數為0.5611,Lee-sigma vs. Mean之相關係數為0.5277,Mean vs. Gamma之相關係數為0.6511,相關性為中度相關,所以並非僅做一種濾波器,得到訓練樣本之最適視窗,即可套用到其他的濾波器處理影像的結果。
主軸轉換後影像以相鄰特性根閾值法決定主軸數,並以不同分類法做比較,結果發現區內分類準確度評估以最大概似法為最好,最佳之結果為結合Mean組織特徵,完全分類正確沒有任何誤分的情形發生,而在區外分類準確度評估以Mahalanobis距離分類法的效能最佳,最佳之結果為結合Lee-sigma組織特徵,OA值11.85%。
Logically, plants have their own particular spectral characteristics which could be applied for tree classification with remote sensing techniques. Unfortunately, most of former researches focus on study landuse classification and have demonstrated that it is not easy to clearly recognize forest vegetation. Spatial information has been proven to be workable in the classification of land covers recently. Tree species identification is more critical and difficult than land covers. A lot of factors affect accurate of tree species identification, but we try to solve the problem. This paper attend to emphasize the potentially valuable signals of remote sensing to detect tree species using a higher spatially resolution of QuickBird images.
The purpose of this paper is identifying tree species using satellite image. We adopt Gray Level Co-occurrence Matrix, Vegetation Index, Gamma texture filter, Lee-sigma texture filter and Mean texture filter to extract new information promote classification accuracy. Than, original five bands image become eighteen bands image to increase more information to tree classification. However, when we apply new image to perform our research and occur Hughes phenomenon, so employ Principle Component Analysis to avoid Hughes phenomenon.
The classified accuracy of QB spectral image performs overall accuracy 60.61%, Kappa hat 0.5915 and assessing accuracy 9.18%. The best combination is MEAN+HOM+CON+DIS+ENT using GLCM method, performs classified result overall accuracy 29.61%, Kappa hat 0.2750 and assessing accuracy 3.83%. The best combination is ARVI+EVI+NDVI+SAVI+VARI using Vegetation Index method, performs classified result overall accuracy 50.86%, Kappa hat 0.4922 and assessing accuracy 7.72%.Using Gamma filter and window size 33*33 processes QB image is the best result, performs classified result overall accuracy 98.66%, Kappa hat 0.9859 and assessing accuracy 8.98%. Using Lee-sigma filter and window size 39*39 processes QB image is the best result, performs classified result overall accuracy 98.06%, Kappa hat 0.9797 and assessing accuracy 6.93%. Using Mean filter and window size 39*39 processes QB image is the best result, performs classified result overall accuracy 98.95%, Kappa hat 0.9890 and assessing accuracy 10.38%.
We also study relationship between training sample size and filter process window size. Processing QB-mss image using Gamma filter, each tree species optimal widow size approximate 1.78±0.80 times of row pixel of training sample and 1.81±0.84 times of column pixel of training sample. Processing QB-mss image using Lee-sigma filter, each tree species optimal widow size approximate 2.03±1.10 times of row pixel of training sample and 2.07±1.19 times of column pixel of training sample. Processing QB-mss image using Mean filter, each tree species optimal widow size approximate 1.62±0.82 times of row pixel of training sample and 1.68±1.00 times of column pixel of training sample. It indicates that training sample and optimal widow size have not significant relationship. Than we compare each tree species optimal widow size among three filter processing result, obtain correlation coefficient of Gamma vs. Lee-sigma, Lee-sigma vs. Mean and Mean vs. Gamma, 0.5611, 0.5277 and 0.6511 respectively. It indicates that using different filter processing image, optimal window size of each training samples is different.
After using principal component analysis, we adopt threshold of slope of adjacent eigenvalues method to determine band number, and compare the classified accuracy of using different methods of image classification. Results show that classified accuracy of training samples using maximum likelihood classifier is the best, and combine Mean texture characteristic the Kappa hat is 1.0. Using assessing samples to evaluate classified accuracy and we find combine Lee-sigma texture characteristic to evaluate classified accuracy using Mahalanobis distance classifier is the best, and overall accuracy is 11.85%.
圖次
表次
中文摘要
英文摘要
第一章 緒論 1
1-1 研究動機與目的 1
1-2論文架構 3
第二章 植生指標 4
2-1 以斜率為基準之植生指標 5
2-1-1 抵抗大氣植生指標 5
2-1-2 規整差植生指標 5
2-1-3 可見光段抵抗大氣植生指標 6
2-2 以距離為基準之植生指標 6
2-2-1 增揚植生指標 7
2-2-2 土壤修正植生指標 7
第三章 組織濾波器 9
3-1 灰階共現矩陣 9
3-2 Gamma組織濾波器 11
3-3 Lee-sigma組織濾波器 11
第四章 影像分類法 13
4-1 光譜分離度 13
4-2 最短距離分類法 15
4-3 最大概似分類法 15
4-4 Mahalanobis距離分類法 16
4-5光譜角分類法 17
4-6 分類精度評估方法 17
第五章 光譜轉換 20
5-1 主成份分析 20
5-2 應用PCA法以避免Hughes現象 21
5-3 主軸數決定方法 22
5-3-1 相鄰特性根斜率閾值法 22
第六章 研究材料 24
6-1 QB衛星介紹 24
6-2 研究材料及試區概述 25
6-3 QB多譜態影像資料特性 26
6-3-1 植物 27
6-3-2 土壤及建築物 27
6-3-3 水體 27
第七章 分析及分類流程 29
7-1 建置立木GIS圖層 29
7-2 GLCM組織特徵萃取 30
7-3 多譜態影像分析 30
7-3-1 大氣糾正 30
7-3-2 引入植生指標及組織濾波器資訊 31
7-4 主軸轉換法 32
第八章 結果與討論 33
8-1 嘉大蘭潭校區立木GIS圖層 33
8-2 QB全色態影像GLCM組織特徵萃取 33
8-2-1 單一GLCM組織特徵 34
8-2-2 結合2個GLCM組織特徵 35
8-2-3 結合3個GLCM組織特徵 35
8-2-4 結合4個GLCM組織特徵 37
8-2-5 結合5個及6個GLCM組織特徵 37
8-2-6 GLCM組織特徵數目與分類精度之關係 38
8-3 QB多譜態影像分析 39
8-3-1 引入植生指標資訊 39
8-3-1-1 單一植生指標分類效能評估 39
8-3-1-2 結合2個植生指標分類效能評估 40
8-3-1-3 結合3個植生指標分類效能評估 41
8-3-1-4 結合4個及5個植生指標分類效能評估 41
8-3-1-5 植生指標數目與分類精度之關係 42
8-3-2 引入組織濾波器資訊 43
8-3-2-1 Gamma組織濾波器 43
8-3-2-1-1 最佳視窗處理大小 43
8-3-2-1-2 訓練樣本最適視窗及樣本大小之關係 52
8-3-2-2 Lee-sigma組織濾波器 57
8-3-2-2-1 最佳視窗處理大小 58
8-3-2-2-2 訓練樣本最適視窗及樣本大小之關係 61
8-3-2-3 Mean組織濾波器 67
8-3-2-3-1 最佳視窗處理大小 67
8-3-2-3-2 訓練樣本最適視窗及樣本大小之關係 70
8-3-3 小結 76
8-4 主軸轉換分析 77
8-4-1 加入Gamma濾波器之最佳處理結果 77
8-4-2 加入Lee-sigma濾波器之最佳處理結果 78
8-4-3 加入Mean濾波器之最佳處理結果 80
8-5 區外分類準確度評估 81
第九章 結論與建議 91
參考文獻 95
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