基于分块PCA与端元提取的壁画线条增强研究

Research on mural line enhancement based on block PCA and endmember extraction

线状特征是壁画中的重要元素。然而受到自然及人为因素的影响,壁画的部分线条常常变得模糊,人眼难以辨别。因此,提出一种利用高光谱影像分块主成分分析(PCA)与端元提取相结合的线状特征增强方法。首先,利用支持向量机(SVM)对壁画的合成真彩色影像进行分类,根据分类结果得到壁画标签数据,实现高光谱影像同质区域的分块数据。其次,对各分块影像进行顶点成分分析(VCA)得到候选端元集,通过构造投影矩阵合并相似端元确定最终端元集。然后,利用非负最小二乘算法解混得到线条丰度图。最后,将分块PCA的第一主成分影像归一化后与线条丰度图进行波段加权平均获取线状特征增强影像,将其与合成真彩色影像进行HSV图像融合得到线状特征融合影像。以瞿昙寺壁画局部高光谱影像为例进行了验证,结果表明,该算法能增强壁画中的线状特征,且较PCA增强法效果更好。

Linear feature is an important element in murals.However,natural or human factors tend to make it difficult for human eyes to distinguish some blurred lines of the murals.Therefore,a linear feature enhancement method using hyperspectral image block principal component analysis(PCA)and image unmixing was proposed.Firstly,the support vector machine(SVM)was employed to classify the hyperspectral composite image of the mural,the result of which could help produce the mural label data.In doing so,the block data of the homogeneous area of the hyperspectral image could be acquired.Secondly,vertex component analysis(VCA)was performed on each segmented image to obtain a candidate endmember set.The final endmember set was determined by constructing a projection matrix and merging similar endmembers.Then,the non-negative least squares unmixing was used to obtain the line abundance map.Finally,the first principal component image of the block principal component analysis was normalized,and band calculation was performed with the line abundance map to obtain the linear feature enhanced image.They were fused with the true color composite image to obtain the linear feature fusion image.Taking some hyperspectral images of murals in Qutan Temple,Qinghai Province,China as an example,the results show that the algorithm can enhance the linear features in the murals,which is superior to the PCA enhancement method.