基于纹理特征的地貌的统计贝叶斯划分方法研究

通过研究“嫦娥一号”拍摄的月球影像灰度图纹理特征，对月貌进行划分，提出一种利用月球纹理特征结合月球影像的灰度值，采用贝叶斯分类法来进行月球地貌的分类。月球影像的纹理特征是由灰度共生矩阵计算出来的13种纹理特征量来刻画的。具体方法是：首先，选择能将不同月球地貌区分开的最佳纹理特征及提取这些最佳纹理特征所采用的相应最优窗口尺寸；其次，对这些提取出的纹理特征进行主成分分析，去除相关性，再运用贝叶斯分类法进行月貌分类。实验表明，该方法能很好地提取出月球表面的纹理特征，并能成功地对月球地貌单元进行自动识别和分类。最后制作完成的月球地貌分区图能为月球的进一步研究提供参考，为更好地探索月球提供详细的资料。

Research on statistical Bayes classification method of geomorphology

This paper divides the moon''s appearance by studying the texture characteristics of the gray-scale image of the moon captured by Chang''e-1. This paper proposes a method of using the lunar texture features combined with the gray value of the lunar image and adopting the Bayesian classification method to classify the lunar landforms. The texture features of the lunar image are described by 13 texture features calculated by the gray-level co-occurrence matrix. The specific method is to first select the best texture features that can distinguish different lunar landforms and the corresponding optimal window size used to extract these best texture features, and then perform principal component analysis on these extracted texture features to remove the correlation, And then use Bayesian classification to classify the appearance of the moon. Experiments show that this method can well extract the texture features of the lunar surface, and can successfully automatically identify and classify lunar landform units. The finished lunar geomorphological zoning map can provide reference for the further study of the moon and provide detailed information for better exploration of the moon.