曾旭平, 阳凡林, 李陶, 赵建虎. 基于SOFM网络的声图非监督分类[J]. 武汉大学学报 ( 信息科学版), 2004, 29(11): 977-980.
引用本文: 曾旭平, 阳凡林, 李陶, 赵建虎. 基于SOFM网络的声图非监督分类[J]. 武汉大学学报 ( 信息科学版), 2004, 29(11): 977-980.
ZENG Xuping, YANG Fanlin, LI Tao, ZHAO Jianhu. Sonar Image Clustering Analysis by SOFM[J]. Geomatics and Information Science of Wuhan University, 2004, 29(11): 977-980.
Citation: ZENG Xuping, YANG Fanlin, LI Tao, ZHAO Jianhu. Sonar Image Clustering Analysis by SOFM[J]. Geomatics and Information Science of Wuhan University, 2004, 29(11): 977-980.

基于SOFM网络的声图非监督分类

Sonar Image Clustering Analysis by SOFM

  • 摘要: 利用局部窗口内的灰度纹理共生矩阵的统计量、灰度均值和两个分维数作为特征矢量,利用SOFM网络进行非监督分类侧扫声纳海底图像,通过实测数据验算,取得了理想的效果。

     

    Abstract: Seabed image is clustered according to SOFM network. The feature vectors are average intensity, six statistics of texture and two dimensions of fractal. It takes the spatial correlation between different pixels and the terrain coarseness into consideration. 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. Dimensions are different in across-track and along-track, so the average of four directions is used to solve this problem.

     

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