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研究生:崔承愷
研究生(外文):Chen-Kai Tsui
論文名稱:表格化K-means遙測影像分群之研究
論文名稱(外文):Tabular K-means Clustering on Remote Sensing Images
指導教授:蔡榮得蔡榮得引用關係
口試委員:林昭宏黃怡碩
口試日期:2017-07-26
學位類別:碩士
校院名稱:國立中興大學
系所名稱:土木工程學系所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:37
中文關鍵詞:K-means群集分析主成分轉換散點圖峰值偵測沃羅諾伊圖查找表
外文關鍵詞:K-meansPrincipal Component TransformationScatter DiagramPeak DetectionVoronoi DiagramLook-Up Table
相關次數:
  • 被引用被引用:2
  • 點閱點閱:311
  • 評分評分:
  • 下載下載:46
  • 收藏至我的研究室書目清單書目收藏:1
隨著電腦科技不斷的進步,電腦硬體的處理速度也不斷的加快,但是遙測影像資料因為感測器之空間解析力、光譜解析力的提升,其數據量也愈來愈龐大,遙測影像的處理、分析、與分類,更需要將有關的傳統演算法進行優化,以使用有限的電腦計算能力,達到加速遙測資料的即時處理功能。
本研究遙發展測影像表格化K-means群集分析方法,將多光譜遙測影像進行主成分轉換,並於前二主成分影像之二維灰階散點圖上偵測峰值做為初始種子,然後應用沃羅諾伊圖對二維主成分影像空間進行分割以建立查找表,以加速遙測影像之非監督性分類。本研究使用Visual C++設計表格化K-means與傳統K-means電腦程式,以7波段Landsat-4 主題測繪器影像進行實驗,結果證明表格化 K-means法比傳統 K-means法更具效率。
The capability in computer hardware processing accelerates as the computer technology evolves. However, the quantity of remotely sensed images explores much faster than computer hardware as the sensor technology evolves with much more spatial resolution and spectral resolution. For real-time data handling of remote sensing images, it demands optimization of traditional algorithms in image processing, spectral analysis, and image classification using limited computer computational capability.
This study develops a Tabular K-means approach for clustering remotely sensed multispectral images. The proposed approach employs principal component transformation, peak detection on two-dimensional (2-D) scatter diagram of the first two principal components as initial seeds, and Voronoi diagram of these seeds in 2-D spectral space for accelerating unsupervised classification of such images. Experimental results from clustering 7-band Landsat-4 Thematic Mapper (TM) images using Visual C++ programs demonstrate that the proposed Tabular K-means performs much more efficiently than the traditional K-means approach.
1. 緒論 1
1.1 研究動機與目的 1
1.2 研究流程 2
1.3 論文架構 3
2. 文獻探討 4
2.1 遙測數位影像 4
2.2 影像的色彩 5
2.3 影像光譜分類技術 6
2.3.1監督性分類 6
2.3.2非監督性分類 7
2.3.3 混合分類法 8
2.4 K-means 演算法與相關研究 8
2.5 沃羅諾伊圖(Voronoi Diagram) 11
2.6 精度評估 12
3. 研究方法 13
3.1傳統K均值法(Traditional K-means) 13
3.2表格化K均值法(Tabular K-means) 16
3.2.1 以主成分轉換縮減處理資料維度 16
3.2.2 以二維散點圖峰值檢測選擇種子點 17
3.2.3 應用沃羅諾伊圖建立查找表 17
3.2.4 修改後步驟 18
4. 實驗結果 20
5. 結論與建議 34
參考文獻 35
蔡榮得,2011,「影像處理」講義,國立中興大學土木工程學系。
繆紹綱,2005,數位影像處理–運用Matlab,台灣東華書局股份有限公司。
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Richards, J. A. and X. Jia, 2006. Remote Sensing Digital Image Processing: An Introduction, 4th ed., Springer.
Schowengerdt, R. A., 1997. Remote sensing: Models and methods for image processing, Academic Press.
Thiessen, H., 1911. Precipitation for large area, Monthly Weather Review, vol. 39, pp. 1082-1084.
Tsai, V. J. D., 1993. Fast Topological Constructions of Delaunay Triangulations and Voronoi Diagrams, Computers and Geosciences, vol. 19, no. 10, pp. 1463-1474.
Tsai, V. J. D. and C. W. Tsai, 2013. GPU-Based Parallelization on Principal Component Transformation, ISRS 2013, Chiba, Japan, 4 p.
Zhu, Y., J. Yu, and C. Jia, 2009. Initializing K-means Clustering Using Affinity Propagation, 2009 9th International Conference on Hybrid Intelligent Systems, Shenyang, China, August 12-14, 2009, 1: 338-343.
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