改良式變異向量分析法於影像變遷之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：39

、訪客IP：18.119.122.140

姓名

陳可薰(Ko-Hsun Chen) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

改良式變異向量分析法於影像變遷之研究
(Modified Change-vector analysis for Change Images)

相關論文

★ 多時期衛星影像之自動化監督性分類	★ 大範圍地區土地使用分類之研究
★ 高解析力衛星影像控制點座標之自動化萃取	★ 影像最佳類別數目之研究
★ 遙控直昇機應用於工程管理監測可行性之研究	★ 以地理資訊系統輔助共同管道之最適設計
★ 有理函數應用於空載多光譜影像幾何校正之研究	★ SPOT自然色影像產生之研究
★ 結合影像區塊及知識庫分類之研究-以IKONOS衛星影像為例	★ 遙控飛機空載視訊影像自動化鑲嵌方法之研究
★ 影像分割技術於高解析衛星影像分類之應用	★ 小波多層次解析之影像融合應用
★ 線性複合模式應用於變遷偵測之研究	★ 改良式變異向量分析法於變遷偵測之探討
★ 區塊分割變遷偵測法於多時期衛星影像之應用	★ 資料挖掘技術應用於外來入侵植物研究 (以恆春地區銀合歡為例)

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

傳統上使用變異向量分析法進行變遷偵測，往往有門檻值給定不客觀的問題，雖然有研究學者利用先行圈選訓練區塊的方式來加以改良，但訓練區圈選的好壞，往往會嚴重影響變遷偵測的成果。因此本研究提出一新方法，改良傳統的變異向量分析法。本研究所提出的改良式變異向量分析法，分為以下兩個步驟： (1) 變遷/非變遷像元位置的偵測：本研究結合變異向量距離與光譜角資訊來進行變遷/非變遷像元的偵測，首先結合光譜角和變異向量距離資訊進行主成分分析，再運用邊緣偵測技術自動化決定第一主成分(PC1)影像的門檻值，即可偵測出變遷與非變遷像元。 (2) 變遷像元類別的判釋: 先計算變異向量之方向角，再運用K-Means演算法對變遷點之方向角進行分類，最後獲得變遷類別的分類影像。本研究利用模擬影像和三組SPOT-5的影像進行測試，在變遷像元偵測的精確度可達95%以上；而在變遷分類的結果，其精確度為87%。實驗測試成果顯示，利用改良式變異向量分析法能有效偵測出影像上變遷像元的位置，並分類出變遷像元的類別。

摘要(英)

Change-vector analysis (CVA) traditionally was applied to detect land cover change by thresholding the difference image according to empirical strategies or manual trial-and-error procedure. It made the result of change detection subjective. Recently training areas are used to guide the selection of the change magnitude of the threshold for discriminating Change and No-Change pixels. However, the result would be greatly affected by the appropriately selected training areas. In this paper, a new method is proposed to improve CVA. The modified change-vector analysis consists of two stages. (1) Change/No-Change detection: The method combines the distance of change vector with spectral angle of change vector to detect Change/No Change pixels. In the first place, the distance of change vector and spectral angle were analyzed by Principal Component Analysis. Then, the edge detection technique was used to find out the optimum threshold automatically in the PC1 image. Therefore the Change and No-Change pixels can be detected. (2) Change categories detection: The direction angles of change vector were calculated beforehand. Then, the direction angles of change vector were classified by the K-Means clustering algorithm to determine change category of the change pixels. In this study, the modified change-vector analysis was applied to the detection of one simulation image and three SPOT-5 satellite images. The results indicated that the overall accuracy of Change/No-Change detection was about 95%, and the overall accuracy of change categories detection was 87%. The experimental results indicate that using the modified change-vector analysis is an effective method to detect Change/No-change pixels and change categories.

關鍵字(中)

★ 變異向量分析法
★ 變異向量方向角
★ 主成分分析
★ 變異向量距離

關鍵字(英)

★ Change-vector analysis
★ Principal Component Analysis
★ the distance of change vector
★ the direction angles of change vector

論文目次

目錄
摘要............................................................................................................ I
Abstract .....................................................................................................II
目錄..........................................................................................................III
圖目錄.....................................................................................................VII
表目錄.....................................................................................................XII
第一章緒論..............................................................................................1
1.1 前言.................................................................................................1
1.2 文獻回顧.........................................................................................2
1.2.1 影像差異法(Image Differencing)…………………..….…….2
1.2.2 影像比例法(Image Ratioing)…………………….……….…4
1.2.3 分類後比較法(Post-classification Comparison)…….……....4
1.2.4 常態化差異植生指標比較法…………………………..……6
1.2.5 主軸轉換分析法………………………………………..……7
1.2.6 變異向量分析法(Change Vector Analysis)……………....…8
1.3 研究目的與方法概述...................................................................13
1.4 章節介紹.......................................................................................14
第二章改良式變異向量分析法............................................................15
2.1 資料前處理...................................................................................16
2.1.1 幾何校正................................................................................16
2.1.2 輻射校正................................................................................17
2.2 資料轉換.......................................................................................17
2.2.1變異向量距離..........................................................................18
2.2.2光譜角……..............................................................................19
2.3主成分分析法.................................................................................20
2.4 PC1影像最佳門檻值之自動化給定.............................................24
2.4.1 邊緣偵測..............................................................................25
2.4.2 決定PC1影像的最佳門檻..................................................26
2.5變遷像元類別的判釋.....................................................................29
第三章測試影像....................................................................................37
3.1模擬影像.........................................................................................37
3.2 SPOT-5變遷影像...........................................................................38
3.2.1 SPOT測試影像I……………………………………….........39
3.2.2 SPOT測試影像II……………………………………………39
3.2.3 SPOT測試影像III………………………………………......40
第四章測試與討論................................................................................42
4.1模擬影像之變遷偵測.....................................................................43
4.1.1 資料前處理………………………………………………....43
4.1.2 變異向量距離與光譜角的轉換………………………..…..43
4.1.3 主成分分析……………………………………..………......44
4.1.4 最佳化門檻值之給定…………………………………..…..45
4.1.5 變遷像元類別的判釋…………………………..………......47
4.2第一組SPOT-5影像測試...............................................................48
4.2.1 資料前處理………………………………...……………….48
4.2.2 變異向量距離與光譜角的轉換………………...………….49
4.2.3 主成分分析…………………………………...………….....50
4.2.4 最佳化門檻值之給定……………………………..………..51
4.2.5 變遷像元類別之判釋……………..………………..….…...54
4.3第二組SPOT-5影像測試..............................................................54
4.3.1 資料前處理……………………………………...………….55
4.3.2 變異向量距離與光譜角的轉換………………………...….56
4.3.3 主成分分析………………………..……………………......57
4.3.4 最佳化門檻值之給定……………………………………....58
4.3.5 變遷像元類別之判釋…………………...……………...…..60
4.4第三組SPOT-5影像測試..............................................................61
4.4.1 資料前處理……………………………………………...….62
4.4.2 變異向量距離與光譜角的轉換…………………………....63
4.4.3 主成分分析……………………………..…………………..64
4.4.4 最佳化門檻值之給定…………………………………...….65
4.4.5 變遷像元類別之判釋…………………..……………...…...68
4.5.精度評估........................................................................................69
4.5.1檢核變遷/非變遷像元偵測之精度..………………….……..69
4.5.1.1模擬影像…………………….……………………..…....69
4.5.1.2 SPOT-5影像………………….………………..………..70
4.5.2 檢核變遷像元分類的精度……………………………...….73
4.5.2.1模擬影像…………………………………..……..……...73
4.5.2.2 SPOT-5影像…………………………………..…..…….74
第五章傳統變異向量分析法之測試…………………...………….…..79
5.1模擬影像測試.................................................................................80
5.1.1 資料轉換………………………..…………………………..80
5.1.2變異向量距離門檻值的給定……………………………….80
5.1.3 變遷像元類別的判釋…………………………..………......82
5.2第一組SPOT-5影像測試...............................................................83
5.2.1 資料轉換………………………………...………………….83
5.2.2 變異向量距離門檻值的給定………………………………84
5.2.3 變遷像元類別之判釋……………..….…….........................86
5.3第二組SPOT-5影像測試...............................................................87
5.3.1 資料轉換………………………...…………..………….......87
5.3.2變異向量距離門檻值的給定……………………...………..88
5.3.3變遷像元類別的判釋…………………..……………………90
5.4第三組SPOT-5影像測試..............................................................91
5.4.1 資料轉換……………………………………………...…….91
5.4.2變異向量距離門檻值的給定………………………….........91
5.4.3變遷像元類別的判釋……………………………………….94
5.5.精度評估........................................................................................95
5.5.1檢核變遷/非變遷像元偵測之精度..………………………...95
5.5.1.1模擬影像…………………….………………………......95
5.5.1.2 SPOT-5影像………………….…………………………95
5.5.2 檢核變遷像元分類的精度………………………...…….....98
5.5.2.1模擬影像…………………………………..…….............98
5.5.2.2 SPOT-5影像……………………………..………….......99
第六章結論與建議..............................................................................104
6.1 結論............................................................................................104
6.2 建議............................................................................................106
參考文獻.............................................................................................108

參考文獻

陳添水(2005)，九九峰地區九二一地震崩塌植生指數變遷分析，特有生物研究，7(2)：63-75。
陳耀茂(2002)，多變量分析導論，全威圖書，台北。
Borrego, R., J. R. Santos, Y. E. Shimabukuro, I. F. Brown, and, H. J. Heinrick, 2001, “A Change Vector Analysis Technique to Monitor Land-use/land-cover in SW Brazilian Amazon: Acre state”, National Institute for Space Research – INPE.
Bruzzone, L., and F. P. Diego, 2000, “Automatic Analysis of the Difference Image for Unsupervised Change Detection”, IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1171-1182.
Byrne, G. F., P. F. Crapper, and K. K. Mayo, 1980, “Monitoring Land-cover Change by Principal Component Analysis of Multitemporal Landsat Data”, Remote Sensing Environ., 10: 175-184.
Chen, J., P. Gong, C. He, R. Pu, and P. Shi, 2003, Land-use/Land-cover Change Detection Using Improved Change-vector Analysis”, Photogrammetric Engineering and Remote Sensing, 69: 369–80.
Crist, E. P., and R. J. Kauth, 1986, “The Tasseled Cap De-mystified”, Photogrammetric Engineering & Remote Sensing, 52: 81-86.
Garcia-Haro F. J., M. A. Gilabert, and J. Melia, 2001, “Monitoring Fire-attected Areas Using Thematic Mapper Data”, International Journal of Remote Sensing, 22(4): 533-549.
Howarth, J. P., and G. M. Wickware, 1981, “Procedure for Change Detection Using Landsat Digital Data”, International Journal of Remote Sensing, 2: 277-291.
Hunter, E. L., and C. H. Power, 2002, “An Assessment of Two Classification Methods for Mapping Thames Estuary Intertidal Habitats using CASI Data”, International Journal of Remote Sensing, 23(15): 2989-3008.
Johnson, R. D., and E. S. Kasischke, 1998, “Change Vector Analysis: A Technique for the Multispectral Monitoring of Land Cover and Condition”, International Journal of Remote Sensing. 19(3): 411-426.
Kauth, R. J., and G. S. Thomas, 1976, “The Tasseled Cap - A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen in Landdat, in Proceedings on the Symposium on Machine Processing of Remotely Sensed Data”, West Lafayette, Indiana, June 29 - July 1, (West Lafayette, Indiana: LARS, Purdue University), 41-51.
Keshava N., 2004, “Distance Metrics and Band Selection in Hyperspectral Processing with Applications to Material Identification and Spectral Libraries”, IEEE Trans. Geosciences and Remote Sensing, 42(7): 1552-1565.
Kruse, F. A., A. B. Lefkoff, J. W. Boardman, K. B.Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Geotz, 1993, “The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imageing Spectromator Data”, Remote Senseing of the Environment, 44: 145-163.
Lillesand, T. M., R. W. Keifer, and J. W. Chipman, 2004, “Remote Sensing and Image Interpretation”, Fifth Edition, John Wiley & Sons.
Lu, D., P. Mausel, M. Batistella, and E. Moran, 2005, “Land-cover Binary Change Detection Methods for Use in the Moist Tropical Region of the Amazon: A Comparative Study”, International Journal of Remote Sensing, 26(1): 101-114
Lyon, J. G., D. Yuan, R. S. Lunetta, and C. D. Elvidge, 1998, “A Change Detection Experiment Using Vegetation Indices”, Photogrammetric Engineering and Remote Sensing 64(2): 143-150.
Macleod, R. D., and R. G. Congalton, 1998, “A Quantitative Comparison of Change-Detection Algorithms for Monitoring Eelgrass from Remotely Sensed Data”, Photogrammetric Engineering and Remote Sensing, 64(3): 207-216.
Michalek, J. L., T. W. Wagner, J. J. Luczkovich, and R. W. Stoffle, 1993, “Multispectral Change Vector Analysis for Monitoring Coastal Marine Environments”, Photogrammetric Engineering and Remote Sensing, 59(3): 381-384.
Nackaerts, K., K. Vaesen, B. Muys, and P. Coppin, 2005, “Comparative Performance of A Modified Change Vector Analysis in Forest Change Detection”, International Journal of Remote Sensing, 26(5): 839-852.
Nelson, R. F., 1983, “Detecting Forest Canopy Change due to Insect Activity Using Landsat MSS”, Photogrammetric Engineering and Remote Sensing, 49: 1303-1314.
Phalke, S. M., and I. Couloigner, 2005, “Change Detection of Linear Man-made Objects and Use of It for the GIS Update”, In the Proceedings of the 98th Annual Conference of the Canadian Institute of Geomatics, Ottawa (ON), June 13-15.
Richards, J. A., 1984, “Thematic Mapping from Multitemporal Image Data Using the Principal Components Transformation”, Remote Sensing Environ., 16: 35-46.
Rubec, C. D., and J. Thie, 1987, “Land Use Monitoring with Landsat Digital Data in Southwestern Manitoba”, Proceedings of the fifth Canadian Symposium on Remote Sensing, Victoria, BC, pp. 136-150.
Schwarz, J., and K. Staenz, 2001, “Adaptive Threshold for Spectral Matching of Hyperspectral Data”, Canadian Journal of Remote Sensing, 27(3): 216-224
Shapiro, A. C., and S. O. Rohmann, 2005, “Summit-to-sea Mapping and Change Detection Using Satellite Imagery: Tools for Conservation and Management of Coral Reefs”, Rev. Biol. Trop. 53: 185-193.
Warner, T., 2005, “Hyperspherical Direction Cosine Change Vector Analysis”, International Journal of Remote Sensing, 26(6): 1201-1215.
Weismiller, R. A., S. J. Kristof, D. K. Scholz, P. E. Anuta, and S. A. Momin, 1977, “Change Detection in Coastal Zone Environments”, Photogrametric Engineering and Remote Sensing, 43(12): 1533-1539.
Wilson, J. R., C. Blackman, and G. W. Spann, 1976, “Land Use Change Detection Using Ladsat Data”, Proceedings of the 5th Annual Remote Sensing of Earth Resources Conference, University of Tennesses, Tullhama, TN, pp.79-91.

指導教授

陳繼藩(Chen Chi-Farn)

審核日期

2006-7-13

推文