| 基于遗传算法的BP网络实现海底底质分类 |
| |
| 引用本文： | 阳凡林,刘经南,赵建虎,独知行.基于遗传算法的BP网络实现海底底质分类[J].测绘科学,2006,31(2):111-114. |
| |
| 作者姓名： | 阳凡林 刘经南 赵建虎 独知行 |
| |
| 作者单位： | 山东科技大学地球信息科学与工程学院,青岛,266510;武汉大学校长办公室,武汉,430079;武汉大学测绘学院,武汉,430079 |
| |
| 基金项目： | “基础地理信息与数字化技术”山东省重点开放实验室资助(SD040212)，国家自然科学基金项目(40474005) |
| |
| 摘 要： | 本文利用灰度纹理共生矩阵和两个分维数作为特征矢量,采用遗传算法训练BP网络,进行海底底质监督分类。以海底侧扫声纳图像为例,通过实测数据验算,取得了理想的效果。
|
| 关 键 词： | BP网络 共生矩阵 分形 分类 遗传算法 |
| 文章编号： | 1009-2307(2006)02-0111-04 |
| 收稿时间： | 2005-05-10 |
| 修稿时间： | 2005年5月10日 |
Seabed classification using BP neural network based on GA |
| |
| YANG Fan-lin,LIU Jing-nan,ZHAO Jian-hu,DU Zhi-xing.Seabed classification using BP neural network based on GA[J].Science of Surveying and Mapping,2006,31(2):111-114. |
| |
| Authors: | YANG Fan-lin LIU Jing-nan ZHAO Jian-hu DU Zhi-xing |
| |
| Abstract: | Side scan sonar imaging is one of the advanced methods for seabed study.In order to be utilized in other projects,such as ocean engineering,the image needs to be classified according to the distributions of different classes of seabed materials.In this paper,seabed image is classified according to BP neural network,and Genetic Algorithm is adopted in train network.The feature vectors are average intensity,six statistics of texture and two dimensions of fractal.It considers not only the spatial correlation between different pixels,but also the terrain coarseness.The texture is denoted by the statistics of the cooccurrence matrix.Double Blanket algorithm is used to calculate dimension.Because a uniform fractal may not be sufficient to describe a seafloor,two dimensions are calculated respectively by the upper blanket and the lower blanket.However,in sonar image,fractal has directivity,i.e.there are different dimensions in different direction.Dimensions are different in acrosstrack and alongtrack,so the average of four directions is used to solve this problem.Finally,the real data verify the algorithm.In this paper,one hidden layer including six nodes is adopted.The BP network is rapidly and accurately convergent through GA.Correct classification rate is 92.5% in the result. |
| |
| Keywords: | BP network co-occurrence matrix fractal classification genetic algorithm |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
