影像分割技術於高解析衛星影像分類之應用

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姓名

莊政斌(Cheng-Bing Chuang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

影像分割技術於高解析衛星影像分類之應用
(Segmentation Technique for Classification of High Spatial Resolution Satellite Images)

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摘要(中)

隨著空間解析度的提升，衛星影像的地面特徵更為清晰可辨，但其地物特性亦趨於複雜，相較於著重單一像素光譜值之方法，使用影像分割技術對產生之區塊進行影像的判釋更為合適。本研究之流程分成影像分割以及區塊分類兩個步驟，在影像分割步驟中，使用密度式分割法，利用影像的光譜特性以及空間關聯性進行分割程序，原始影像經影像分割步驟後產生由眾多區塊組成的區塊影像。在區塊分類步驟中，利用知識庫系統，對區塊影像中的各個區塊進行類別的判釋。本研究設計兩組知識庫，一組僅以光譜特性來建立，另一組使用光譜以及紋理兩種特性來建立，分析加入紋理資訊後，其對於影像分類的助益。在影像分割步驟中，使用密度式分割法能簡化影像分割後之區塊數量，降低區塊影像之複雜度，並能提升後續的處理效率，在區塊分類步驟中，利用知識庫系統，能依據影像特性設計多種知識一起進行分析，更有利於影像的分類。本研究使用IKONOS衛星影像作為測試資料，以相同地區之航照判釋土地利用圖作為分類精度評估之依據。成果顯示，僅使用光譜知識庫進行分類之整體精度約為82%，加入光譜資訊後之分類精度可提升為87%，說明紋理資訊對於影像分類具有實質的助益。在高解析衛星影像分類中，使用影像分割方法進行分析是合適的。

摘要(英)

As the spatial resolution increases, satellite images are able to provide better land-cover discrimination. However, the content of high resolution images becomes more complicated, pixel-by-pixel image analysis method is not applicable. Therefore, we use image segmentation technique in image process. In this study, the image classification process consists of two steps: image segmentation and object classification. In the first step, algorithm of density-base segmentation is applied, which takes account of spatial connection and the similarity of spectrum space. The process will group pixels into numerous regions and transform the source image into an object image. The second step is designed to classify every region. The classification is based on knowledge-base system which was designed by spectrum and texture information. In this study, we design different knowledge-base system. One of the knowledge-base system consists of spectrum characteristics while the other consists of both spectrum and texture characteristics. The test data of this study are IKONOS satellite images, and the ground truth obtained from aerial photographs is utilized as references for accuracy assessment. The classification accuracy when we use the knowledge-base system consists of only spectrum characteristics is approximately 82%. The classification accuracy e when the knowledge-base system takes texture information into consideration is approximately 87%. For this study, high resolution satellite image classification using segmentation technique and knowledge-base system is applicable.

關鍵字(中)

★ 知識庫
★ 影像分割

關鍵字(英)

★ Image Segmentation
★ Knowledge-base System

論文目次

摘要 Ⅰ
Abstract Ⅱ
目錄 Ⅲ
圖目錄 Ⅴ
表目錄 Ⅷ
第一章緒論 1
1-1 研究動機及目的 1
1-2 文獻回顧 2
1-2-1 影像分割 2
1-2-2 區塊分類 7
1-3 研究方法及流程 10
1-4 章節簡介 10
第二章影像分割 12
2-1 影像分割簡介 12
2-2 影像分割方法 12
2-2-1 密度概念 13
2-2-2 密度式分割法之空間關聯性 15
2-2-3 密度式分割法產生之區塊編號 16
2-3 影像分割流程 17
2-3-1 密度式分割法流程及參數說明 17
2-3-2 密度式分割法後處理 21
2-3-3 密度式分割法參數之影響性 24
第三章區塊分類 29
3-1 區塊分類簡介 29
3-2 模糊理論應用於知識庫 30
3-2-1 模糊值 30
3-2-2 模糊操作元 34
3-3 本研究使用之知識庫特性說明 36
3-3-1 可建立之特性 36
3-3-2 灰階共現矩陣 37
3-3-3 紋理特徵統計 39
3-3-4 建立之特性數量說明 40
第四章測試影像與測試成果 41
4-1 測試影像資料簡介 41
4-2 測試影像 42
4-2-1 測試影像Ⅰ 42
4-2-2 測試影像Ⅱ 50
4-3 精度分析 58
4-3-1 土地利用現地調查圖 58
4-3-2 精度評估 64
第五章結論與建議 69
5-1 結論 69
5-2 建議 70
參考文獻 72

參考文獻

莊雲翰, 2002, “結合影像區塊及知識庫分類之研究—以IKONOS衛星影像為例”,碩士論文, 國立中央大學土木工程學系研究所
王文俊編著, 認識Fuzzy, 全華科技圖書, 1986
唐德誠, 2002, “灰度共現矩陣於多波段多極化SAR影像分類之研究”, 碩士論文, 國防大學中正理工學院軍事工程研究所
莊家和, 2002, “紋路特徵編碼法於紋路分析之研究”, 碩士論文, 南華大學資訊管理學系碩士班
Antunes, A., Lingnau, C., and Silva, J.C., 2003, “Object oriented analysis and semantic network for high resolution image classification”, Anais XI SBSR, Belo Horizonte, Brasil, 05-10 abril 2003, INPE, pp. 273-279.
Baatz, M., and Schape, A., 2000, “Multiresolution Segmentation – an optimization approach for high quality multi-scale image segmentation”, Angewandte Geographische Informationsverarbeitung XII, Beitrage zum AGIT-Symposium Salzburg 2000, Karlsruhe, Herbert Wichmann Verlag : 12-23.
Cheng, S.-C., 2003, “Region-growing approach to colour segmentation using 3D clustering and relaxation labelling”, Vision, Image and Signal Processing, vol. 150, pp.270-276.
Darwish, A., Leukert, K., and Rrinhardt, W., 2003, “Image Segmentation for the Purpose of Object-Based Classification”, Proceedings of IGARSS 2003 IEEE, July 2003, Toulouse.
DEFINIENS, eCognition object oriented image analysis, User Guide 3
http://www.definiens-imaging.com/index.htm
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X., 1996, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases With Noise”, Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining.
Haralick, R., and Shapiro, L., 1985, “Survey : image Segmentation Techniques”, Computer Vision, Graphics, and Image Processing, vol. 29, pp. 100-132.
Hofmann, P., 2001, “Detecting urban features from IKONOS data using an object-oriented approach”, Remote Sensing and Photogrammetry Society(Editor): Proceedings of the First Annual Conference of the Remote Sensing and Photogrammetry Society 12-12 September 2001, 28-33.
Lillesand, T.M. and Kiefer, R.W., 1999, Remote Sensing and Image Interpretation, 4th Ed., John Wiley and Sons, Inc.: Toronto
Marceau, J., Howarth, J., and Dubois, M., 1990, ”Evaluation of the Grey-Level Co-Occurrence Matrix Method For Land-Cover Classification Using SPOT Imagery”, IEEE Trans. Geosci. Remote Sensing, vol. 28, no. 4, pp. 513-519.
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.
Pathak, V., and Dikshit, O., 2003, “Segment based classification using IRS-1C, LISS-III data”, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, vol. 6, pp. 3513-3515.
Richards, J., and Jia, X., 1999, “Remote Sensing Digital Image Analysis : an introduction”, Speinger-Verlag Berlin Heidelberg New York.
Sappa, A.D., and Devy, M., 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., Petrou, M., and Kittler, J., 1997, “Automatic watershed segmentation of randomly textured color images”, IEEE Trans. Image Processing, vol. 6, no. 11, pp. 1530-1543.
Song, M., and Civco, D.L., 2002, “A knowledge-based approach for reducing cloud and shadow”, Proc. 2002 ASPRS Annual Convention, Washington, D.C., 7p.
Sander, J., Ester, M., Kriegel, H.-P., and Xu, X., 1998, “Density-Based Clustering in Spatial Databases : The Algorithm GDBSCAN and its Application”, Data Mining and Knowledge Discovery, vol. 2, pp. 169-194.
Tuduki, Y., Murase, K., Izumida, M., Miki, H., Kikuchi, K., Murakami, K., and Ikezoe, J., 2000, “Automated seeded region growing algorithm for extraction of cerebral blood vessels from magnetic resonance angiographic data”, Engineering in Medicine and Biology Society, vol. 3, pp. 1756-1759.
Ye, Q., Gao, W., and Zeng, W., 2003, ”Color Image Segmentation Using Density-Based Clustering”, Proceedings of IEEE International Conference on Acoustics, Speech, & Signal Processing, April 6-10.

指導教授

陳繼藩(Chi-Farn Chen)

審核日期

2004-7-9

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