發(fā)布時間：2018-10-17 12:51

【摘要】：近年來,在航運中泄漏到海洋與內河河流中的數萬噸石油對周邊環(huán)境造成了極其嚴重的污染。在海上溢油監(jiān)測技術領域,國內外已取得了矚目的成績。然而,內河流域因其水文環(huán)境復雜,現有溢油監(jiān)測技術仍無法應對突發(fā)的溢油事故。本論文以溢油紋理特征為出發(fā)點,根據"強光照下,油膜和水面呈現不同視覺效果"這一特性,通過紋理特征提取方法,分別對油膜和水面紋理進行特征提取,得到特征量。以紋理特征量為數據源,利用支持向量機分類原理,預測油膜和水面紋理圖像分類準確率。對比不同提取方法下得到的預測準確率。預測準確率越高,說明紋理特征量包含的油膜和水面紋理特征越精確,越有助于后期溢油圖像的監(jiān)測識別工作�；谟湍ず退婕y理特征,本論文對特征提取中經典的灰度共生矩陣方法進行了詳細說明。Haralick從該矩陣中提取了 14個特征量,本文從中選擇了能夠表征油膜和水面紋理特征的特征量:角二階矩、對比度、相關性、熵以及逆差矩,然后在灰度共生矩陣的基礎上,衍生出一維灰度共生矩陣方法。根據油膜紋理的彩色特性,將灰度共生矩陣和顏色信息相結合,獲得顏色共生矩陣,并由此衍生出一維顏色共生矩陣、各分量顏色共生矩陣等方法。根據HSI空間下溢油紋理特征,本文提出一種基于色調和飽和度分量的提取方法——色調飽和度共生矩陣法。利用上述方法提取油膜和水面紋理特征量,最終獲得預測準確率。對比準確率,分析各方法優(yōu)劣性。實驗結果表明,針對強光照下的溢油紋理特征,從顏色共生矩陣和色調飽和度共生矩陣方法中提取的紋理特征量具有更高的分類準確度。顏色信息、像素空間關系信息以及色調飽和度分量信息在表征油膜和水面紋理特征方面具有重要的研究參考價值,可用于后續(xù)強光照下內河溢油的監(jiān)測、識別工作。
[Abstract]:In recent years, tens of thousands of tons of oil leaking into the ocean and inland rivers in shipping have caused extremely serious pollution to the surrounding environment. In the field of offshore oil spill monitoring technology, domestic and foreign has made remarkable achievements. However, due to its complex hydrological environment, the existing oil spill monitoring technology is still unable to cope with sudden oil spill accidents. In this paper, based on the feature of oil spill texture, according to the feature of "oil film and water surface show different visual effects under strong light", the oil film and water surface texture are extracted by the method of texture feature extraction, and the feature quantity is obtained. The classification accuracy of oil film and water surface texture images is predicted by using support vector machine (SVM). The prediction accuracy of different extraction methods is compared. The higher the prediction accuracy is, the more accurate the oil film and surface texture feature included in the texture feature quantity is, and the more helpful to monitor and identify the oil spill image. Based on oil film and surface texture features, the classical gray level co-occurrence matrix method in feature extraction is described in this paper. Haralick extracts 14 feature quantities from the matrix. In this paper, we select the characteristics of oil film and water surface texture: angular second order moment, contrast, correlation, entropy and deficit moment, and then derive one dimensional gray level co-occurrence matrix method based on gray level co-occurrence matrix. According to the color characteristics of oil film texture, the color-co-occurrence matrix is obtained by combining the gray level co-occurrence matrix with the color information, and the one-dimensional color co-occurrence matrix and each component color-co-occurrence matrix are derived. Based on the texture features of oil spill in HSI space, a new extraction method based on hue and saturation components is presented in this paper, which is called hue saturation co-occurrence matrix method. The oil film and surface texture features are extracted by the above methods, and the prediction accuracy is obtained. Compare the accuracy and analyze the advantages and disadvantages of each method. The experimental results show that the texture features extracted from the color co-occurrence matrix and the hue saturation co-occurrence matrix method have higher classification accuracy for the oil spill texture features under strong illumination. Color information, pixel spatial information and hue saturation information have important reference value in the characterization of oil film and water surface texture features, and can be used to monitor and identify the oil spill of inland rivers under the following strong illumination.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：2276717