强光照下内河溢油纹理特征提取研究
[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.
【学位授予单位】:大连海事大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:TP391.41
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