| 基于SVM算法和GLCM的侧扫声纳影像分类研究 |
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| 引用本文： | 郭军,马金凤,王爱学.基于SVM算法和GLCM的侧扫声纳影像分类研究[J].测绘与空间地理信息,2015(3):60-63. |
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| 作者姓名： | 郭军 马金凤 王爱学 |
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| 作者单位： | 1. 广州海洋地质调查局，广东广州510760; 国土资源部海底矿产资源重点实验室，广东广州510075;2. 武汉大学测绘学院,湖北武汉,430079 |
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| 基金项目： | 广州海洋地质调查局天然气水合物专项数据库建设及战略研究( GZH201100312-01)；国土资源部海底矿产资源重点实验室开放基金 |
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| 摘 要： | 根据侧扫声纳影像的特征,提出一种基于SVM和GLCM的侧扫声纳影像分类方法,利用灰度共生矩阵提取其纹理特征,采用主成分分析法对纹理特征进行筛选,选择适合侧扫声纳影像的最佳纹理特征,结合侧扫声纳影像的回波强度,应用支持向量机对侧扫声纳影像进行分类。研究结果表明,纹理特征结合回波强度的支持向量机分类精度高于只依靠回波强度的支持向量机分类精度。
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| 关 键 词： | 侧扫声纳图像 支持向量机 灰度共生矩阵 纹理分析 |
Study of Side Scan Sonar Image Classification Based on SVM and Gray Level Co-Cccurrence Matrix |
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| GUO Jun,MA Jin-feng,WANG Ai-xue.Study of Side Scan Sonar Image Classification Based on SVM and Gray Level Co-Cccurrence Matrix[J].Geomatics & Spatial Information Technology,2015(3):60-63. |
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| Authors: | GUO Jun MA Jin-feng WANG Ai-xue |
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| Institution: | GUO Jun;MA Jin-feng;WANG Ai-xue;Guangzhou Marine Geological Survey;Key Laboratory of Marine Mineral Resources,Ministry of Land and Resources;School of Geodesy and Geomatics,Wuhan University; |
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| Abstract: | According to the features of sonar image, a methodology of side scan sonar image classification using support vector machine and gray level co-occurrence matrix is presented.The texture features extracted with gray level co-occurrence matrix and principal component analysis method, and then we give out the most suitable the texture features for side scan sonar image.Sea classification is carried out with support vector machine using the backscatter combined with sonar image.It is concluded that the result of classifica-tion based on the backscatter combined with the texture features is better than that based on solely the backscatter. |
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| Keywords: | side scan sonar image support vector machine gray level co-occurrence matrix texture analysis |
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