土地覆蓋和土地利用的變遷偵測是遙測影像上一項重要的應用。影像變遷偵測有兩個重要的工作：(1)變遷區域之偵測(2)類別變化之偵測。過去在傳統上主要是以像元為基礎的方式來進行變遷區域偵測的工作，在偵測的結果上往往會出現許多如像元大小般的雜訊。因此，在本研究中提出以區塊為基礎的技巧，來進行變遷偵測的工作。本研究主要的方式是對多時期影像先進行合併的工作，再利用區塊增長法(region growing)對於合併後的影像進行區塊化的分割，接著再應用統計上的顯著水準測試來檢驗區塊有無變遷情形發生。最後，利用資料挖掘(data mining)之技術建立類別知識庫，來尋變遷區塊之類別變化情形。此方法實際運用在模擬影像及SPOT5影像上進行實驗的測試與評估，其結果顯示本研究所提出以區塊為基礎及資料挖掘技術的變遷偵測方式，可提供完整的變遷區塊及類別變遷的資料。

Land cover/land use change detection has been an important application of remotely sensed images. Basically, a practical change detection algorithm is expected to provide a complete information about the change that includes changed locations and categories. Unlike the pixel-based change detection methods, which normally generate pixel-sized noises, this study develops a segmentation-based change detection technique. The main idea of the study is to apply region growing segmentation to multi-dated composite images, and divide the images into many regions. Then a statistical significance test is used to find out the regions that have been changed or not. Thereafter, a knowledge-based system developed by the data mining technique is used to find out the category change for each changed region. An experiment is performed to detect the changed areas with simulated and SPOT5 images, and the result shows that the segmentation-based change detection method combined with data mining technique can provide region-liked changed map as well as the change information about the category.

黃俊英 (1985) "多變量分析", 中國經濟企業研究所,華泰文化事業公司
王文俊 編著, 認識Fuzzy, 全華科技圖書, 1986
徐維政 (2003) ”改良式變異向量分析法於變遷偵測之探討” ，碩士論文， 國立中央大學土木工程研究所
張紘炬 (2002) ”統計學”，華泰書局
Berry, M. and G. Linoff, (1997) “Data Mining Techniques for Marketing, Sales, and Customer Support,” John Wiley & Sons
Byrne, G., P. Crapper, and K. Mayo, (1980)“Monitoring Land-cover Change by Principal Component Analysis of Multitemporal Landsat Data”, Remote Sensing Environ., Vol.10, pp.175-184
DEFINIENS, eCognition object oriented image analysis, User Guide 3
http://www.definiens-imaging.com/index.htm
Haralick, R., and L. Shapiro, (1985) “Survey : Image Segmentation Techniques”, Computer Vision, Graphics, and Image Processing, vol. 29,
pp. 100-132.
Hastie, T. ; R.Tibshirani, ; J. Friedman, (2001) "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", Spring Series in Statistics.
Jensen, J. R., K. Rutchey, M. S. Koch, S. Narumalani , (1995),”Inland Wetland Change Detection in the Everglades Water Conservation Area Using a Time Series of Normalized Remotely Sensed Data”, Photogrammetric Enginneering & Remote Sensing ,Vol. 61,No. 2,pp.199~209.
Kruse, F., A. Lefkoff, J. Boardman, K. Heidebrecht, A. Shapiro, P. Barloon and A. H. Geotz, (1993) “The Spectral Image Processing System (SIPS) – Interactive Visualization and Analysis of Imageing Spectromator Data”, Remote Senseing of the Environment, vol.44, pp 145-163.
Lillesand, T., and R. Keifer, (2004) “Remote Sensing and Image Interpretation”, Second Edition, John Wiley & Sons
Maselli, F., A. Rodolfi , C. Conese , (1996)” Fuzzy Classification of Spatially Degraded Thematic Mapper Data for the Estimation of Sub-Pixel Components”, International Journal of Remote Sensing,Vol. 17,No. 3,pp.537~551.
Pekkarinen, A.,( 2002) “A Method For the Segmentation of Very High Resolution Images of Forested Landscapes”, International Journal of Remote Sensing, vol. 23, no. 14, pp. 2817-2836.
Richards, J., (1984)“Thematic Mapping from Multitemporal Image Data Using the Principal Components Transformation”, Remote Sensing Environ., Vol.16
Richards, J., and X. Jia, (1999) “Remote Sensing Digital Image Analysis : an Introduction”, Speinger-Verlag Berlin Heidelberg New York.
Rubec, C., 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
Sappa, A., and M. Devy, (2001) “Fast Range Image Segmentation by an Edge Detection Strategy”, IEEE 3rd International Conference on 3-D
Digital Imaging and Modeling, Quebec City, Canada.
Shafarenko, L., M. Petrou, , and J. Kittler,( 1997) “Automatic Watershed
Segmentation of Randomly Textured Color Images”, IEEE Trans. Image
Processing, vol. 6, no. 11, pp. 1530-1543.
Singh, A., ( 1986) “Change Detection in the Tropical Forest Environmental of Northern India Using Landsat”, Remote Sensing and Tropical Land Management,M.J. Eden and J.T. Parry, Eds. John Wiley & Sons, London, pp.237-254
Stauffer, M. and R. McKinney, (1978)“Landsat Image Differencing as an Automated Land Cover Change Detection Technique”, Computer Sciences Corporation, Technical Memorandum CSC/TM-78/6215 Silver Spring, MD
Stow, D. , L. Tinney, and J. Estes, (1980) “Deriving Land Use/Land Cover Change Statistics form Landsat: A Study of Prime Agricultural Land”,Proceeding of the 14th International Symposium on Remote Sensing of Environment, pp. 1227-1237
Weismiller, R., S. Kristoof, D. Scholz, P. Anuta, and S.A. Momen, (1977) “Change Detection in Coastal Zone Environments”, Photogrammetric Engineering and Remote Sensing”, Vol.43, pp.1533-1539
Yamamoto, T., and H. Hiroshi, (2001) “A Change Detection Method for Remotely Sensed Multispectral and Multitemporal Image Using 3-D Segmentation”, IEEE Trans. Geosci. Remote Sensing, Vol.39 , No.5, May