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。
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.
出处
《测绘科学》
CSCD
北大核心
2006年第2期111-114,共4页
Science of Surveying and Mapping
基金
"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)
关键词
BP网络
共生矩阵
分形
分类
遗传算法
BP network
co-occurrence matrix
fractal
classification
genetic algorithm
