光學衛星影像於海洋異常物偵測之研究

Ocean Anomaly Detection Using Optical Satellite Images

柯弈仲(Yi-Chung Ke)

指導教授 ： 陳繼藩

國立中央大學/工學院/土木工程學系/碩士(2008年)

摘要

由於海洋汙染對生態迫害及經濟損失的衝擊甚大，快速且準確的找出海洋污染區域是迫切需要的。隨著遙測技術的進步，衛星影像已被廣泛地使用於偵測海洋污染(異常物)。而異常物與背景物的反射率有顯著的不同，本研究的目的就是利用此特性進行自動化海上異常物偵測(anomaly detection)。
本研究方法，分成三大步驟：(1) 首先利用 RX ( Reed & Xiaoli )演算法產生異常物強度影像，影像中強度值越高的像元，屬於異常物的機率越高；(2) 將異常物強度影像利用期望值最大化演算法 (Expectation-Maximization, EM)，求得異常物和背景兩類各自的機率密度函數，基於貝氏理論 (Bayes Rule) 在兩曲線相交的地方，兩類別誤差合為最小，則為最佳門檻值，高於門檻值部分視為異常物，反之則為背景，進而產生初始二值化影像；(3) 選定誤授率相對較低的像素的異常物點作為種子點，利用區域成長法( Region Growing )在初始二值化影像上，找出異常物主體部分，得到成果影像。
本研究使用SPOT-4、SPOT-5 及福衛二號光學衛星影像作為測試資料，和傳統給定門檻值方法及人工數化成果比較後，顯示本研究方法可以自動化且完整偵測出海上異常物，同時能快速的得到異常物主體形狀、面積以及坐標，利於使用者決策及解決問題，免去人工數化之繁瑣過程。

關鍵字

異常物偵測 ； RX演算法 ； 期望值最大化演算法 ； 區域成長法

並列摘要

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.

並列關鍵字

EM (Expectation-Maximization) ； Anomaly Detection ； Region Growing ； RX algorithm

參考文獻

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