由於山林的濫墾以及山地違建的情況，土石流名列台灣山區重大災害前茅。
為了預防土石流的發生，如何正確的取得山區的開發狀況是相當重要的。在本篇論文中，植基於各種影像處理技術：色彩空間轉換、伽瑪校正、機器學習策略以及區域成長法等技術，我們發展出一套自動化的山區植被變異以及違建偵測系統。我們的系統首先針對不同年份相同位置的彩色空照影像，進行植被區域和非植被區域的偵測，並透過偵測結果來分析在期間內有發生變異的植被與非植被區域，以將正確的山區開發狀況提供給相關管理單位，系統亦提供道路及廣場人機互動偵測。除此之外，我們也結合了資料庫應用，將偵測的結果儲存於資料庫中，以供長期分析之用。

Mudflows and landslides has been the major disaster of mountains in Taiwan due to the increase of cultivating farms, deforestation in areas, and squatters. In order to prevent mudflows and landslides, how to obtain correct information about the development of mountain areas is very important. In this thesis, based on various image processing techniques, , such as the color space transform, gamma correction, machine learning method, region growing, etc., we develop an automatic vegetation change and squatter detection system for mountain areas. Our developed system first detects vegetation and non-vegetation regions of two aerial images in the same position of motion area but different years. From the detected result, we analyze these two parts for locating changing sub-regions of vegetation and non-vegetation regions to provide a guideline to the relevant management. Our system also provides a human-computer interaction-based method for detecting roads and squares in mountain areas. Further, combining the database application, our detection results are recorded in the database for the long-term analysis.

[1]A. A. Goshtasby, 2-D and 3-D image registration, Wiley-Interscience, 2004.

[2]G. Moser, S. Serpico, G. Vernazza, “Unsupervised change detection from multichannel SAR images,” IEEE Geoscience and Remote Sensing Letter, 40(2), 2007, pp.278-282.

[3]R. Dianat, S. Kasaei, “Change Detection in Optical Remote Sensing Images Using Difference-Based Methods and Spatial Information,” IEEE Geoscience and Remote Sensing Letters,7(1), 2010, pp.215-219.

[4]T. Celik, “Multiscale Change Detection in Multitemporal Satellite Images,” IEEE Geoscience and Remote Sensing Letters, 6(4), 2009, pp.820-824.

[5]K. I. Ranney, M. Soumekh, “Signal subspace change detection in averaged multilook SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, 11(1), 2006, pp.201-213.

[6]J. Inglada, G. Mercier, “A new statistical similarity measire for change detection in multitemporal SAR images and its extension to multiscale chane analysis,” IEEE Transactions on Geoscience and Remote Sensing, 45(5), 2007, pp. 1432-1445.

[7]Y. Bazi, L. Bruzzone, F. Melgani, “Automatic Identification of the number and values of decision thresholds in the log-ratio image for change detection in SAR images,” IEEE Geoscience and Remote Sensing Letter, 3(3), 2006, pp.349-353.

[8]O. Miller, A. Pikaz, A. Averbuch, “Objects based change detection in a pair of graylevel images,” Pattern Recognition, 38(11), 2005, pp.1976-1992.

[9]鍾國亮, 影像處理與電腦視覺, 第四版, 東華書局, 2008。

[10]R. W. G. Hunt, Measuring Colour, Second Edition, Ellis Horwood, 1991.

[11]R. W. G. Hunt, Measuring Colour, Third Edition, Fountain Press, 1998.

[12]F. Y. Shin, S. Cheng, “Automatic seeded region growing for color image segmentation,” Image and Version Computing, 23(10), 2005, pp.877-886.

[13] Y. Deng, B.S. Manjunath, “Unsupervised segmentation of colortexture regions in images and video, ” IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8), 2001, pp.800-810.

[14] J. Fan, D. K.Y. Yan, A. K. Elmagarmid, W.G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing, ” IEEE Transactions on Image Processing, 10(10), 2001, pp.1454-1466.

[15] H. D Cheng, X. H. Jiang, Y. Sun, J. Wang, “Color image segmentation: advance and prospects,” Pattern Recognition, 34(12), 2001, pp.2259-2281.

[16] C. Garcia, G. Tziritas, “Face detection using quantized skin color regions merging and wavelet packet analysis,” IEEE Transactions on Multimedia, 3(1), 1999, pp.264-275.

[17]D. Hearn, M. P. Backer,Computer Graphics,Second Edition, Prentice Hall, New York, 1994.

[18]J. Scharcanski, A. N. Venetsanopoulos, “Edge Detection of Color Images Using Directional Operators,” IEEE Transactions on Circuits and System For Video Technology, 7(2), 1997, pp.397-401.

[19]R. M. Haralick, L.G. Shapiro, “Imgae Segmentation Techniques,” Computer Vision, Graphics, and Image Processing, 29(1), 1985, pp.100-132.

[20] J. Huang, W. Xie, and L. Tang, “Detection of and compensation for shadows in colored urban aerial images,” in Proceeding of the fifth World Congress on Intelligent Control and Automation, Hangzhou, China, Vol. 4, 2004, pp.3098–3100.

[21] V. J. D. Tsai, “A comparative study on shadow compensation of color
aerial images in invariant color models,” IEEE Transactions on Geoscience and Remote Sensing, 44(6), 2006, pp.1661–1671.

[22] K. L. Chung, Y. R. Lin, Y. H. Huang, “Efficient Shadow Detection of Color Aerial Images Based on Successive Thresholding Scheme,” IEEE Transactions on Geoscience and Remote Sensing, 47(2), 2009, pp.671-682.

[23] [online] Available:
http://www.6law.idv.tw/6law/law3/%B9H%B3%B9%AB%D8%BFv%B3B%B2z%BF%EC%AAk.htm

[24] [online] Available:
http://www.microsoft.com/en/us/default.aspx