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Student Number 953202070
Author Yi-Chung Ke(柯弈仲)
Author's Email Address 953202070@cc.ncu.edu.tw
Statistics This thesis had been viewed 1511 times. Download 1205 times.
Department Civil Engineering
Year 2007
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title Ocean Anomaly Detection Using Optical Satellite Images
Date of Defense 1998-06-07
Page Count 102
Keyword
  • Anomaly Detection
  • EM (Expectation-Maximization)
  • Region Growing
  • RX algorithm
  • Abstract Since the ocean pollution usually causes severe damage to the environment and economy, it is the important issue for detecting the pollution rapidly. The satellite imagery could provide observations of wide areas; it can be used for detecting the anomaly which may be the pollution on the ocean. Generally, the anomaly is defined as the object with different characteristics of reflectance from the background. Base on this definition, the automatic algorithm could be developed to detect the anomaly.
    In this study, a three-stage algorithm is proposed to detect the anomaly automatically: (1) Use RX (Reed & Xiaoli) algorithm to derive the RX image which represents the intensity of the anomaly. (2) Use EM (Expectation-Maximization) algorithm to classify the image into two probability distribution functions which represents the anomaly and background respectively. Then a threshold is determined to binarize the image to show up the anomaly. (3) In order to reduce the noise, the region growing algorithm is used to refine the anomaly image.
    Various satellite images are used to test the proposed algorithm. The results show that the shape, area, and location of ocean anomaly could be observed clearly and accurately. Furthermore, the accuracy information could be estimated to evaluate the result.
    Table of Content 摘要i
    Abstractii
    誌謝iii
    目錄iv
    圖目錄vii
    表目錄xi
    第一章前言1
    1-1研究背景與目的1
    1-2文獻回顧3
    1-2-1海洋異常物偵測3
    1-2-2RX 演算法4
    1-2-3門檻值選定5
    1-2-4期望值最大化演算法( Expectation-Maximization, EM )7
    1-2-5區域成長法( Region Growing )8
    1-3研究內容與論文架構9
    第二章研究方法10
    2-1RX 演算法11
    2-2門檻值之選定13
    2-3期望值最大化演算法(Expectation-Maximization)14
    2-4空間過濾濾除雜訊17
    2-4-1區域成長法( Region Growing )18
    2-5成果分析20
    第三章測試資料介紹23
    3-1測試影像介紹23
    第四章研究成果與分析32
    4-1本研究成果32
    4-1-1RX 演算法成果32
    4-1-2EM 演算法成果38
    4-1-3利用空間過濾消除雜訊成果49
    4-2傳統方法與本研究方法成果比較62
    4-3人工數化成果與本研究方法成果比較73
    第五章結論與建議81
    5-1結論81
    5-2建議82
    參考文獻     84
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    Advisor
  • Chi-Farn Chen(陳繼藩)
  • Files
  • 953202070.pdf
  • approve immediately
    Date of Submission 2008-07-17

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