基于多尺度超像素分割的壁画多光谱图像颜料分类方法

Pigment Classification Method of Mural Multi-Spectral Image Based on Multi-Scale Superpixel Segmentation

在壁画多光谱图像颜料分类中,传统算法通常通过固定窗格提取图像的空间特征,而忽略了不同颜料之间的空间关系,对晕染区域颜料分类误差较大,同时,单一尺度的特征提取方法无法有效表达颜料色块之间的差异。对此,提出一种基于多尺度超像素分割的壁画多光谱图像颜料分类方法。首先,利用自适应波段优选方法对壁画多光谱数据进行降维,有效减少超像素分割的数据量;其次,对经波段优选降维后前3个波段合成的伪彩色图像进行基于梯度约束的超像素分割,使分割结果更贴近实际轮廓,从而提高颜料分类精度;然后,将选取的样本像素点映射到超像素内实现图像空间信息和特征增强;最后,针对单一尺度无法准确适用于每种颜料色块的问题,利用多尺度超像素对壁画伪彩色图像进行分割,获取不同尺度的分割图,在分割图的同一超像素标签区域内进行均值滤波,再利用支持向量机(SVM)分类器对多尺度超像素分割图像进行颜料分类。此外,采用基于多数投票的决策融合策略获取最终分类结果。实验结果表明,该方法在模拟壁画多光谱图像数据集上达到了98.84%的总体精度和97.75%的平均精度,相较于对照组即其他传统分类算法,能够提供更为精确的分类结果。

In pigment classification of mural multi-spectral image,traditional algorithms typically extract the spatial features of the image through the fixed pane.Specifically,the spatial relationship between different pigments is ignored,and the classification error of pigments in the halo area is large.Furthermore,the feature extraction method of a single scale cannot effectively express the differences between pigment blocks.In this study,a pigment classification method for mural multi-spectral images based on multi-scale superpixel segmentation is proposed.First,the dimensionality of mural multi-spectral data is reduced by using adaptive band optimization method,which effectively reduces the amount of data required for superpixel segmentation.Second,the pseudo-color image synthesized by the first three bands after the band optimization and dimensionality reduction is segmented based on gradient constraint.It leads to segmentation results that are more close to the actual contour and improves the accuracy of pigment classification.Third,the selected sample pixels are mapped into the super pixels to realize the spatial information and feature enhancement of the image.Finally,given that a single scale cannot be accurately applied to each pigment block,multi-scale superpixels are used to segment falsecolor mural images,obtain segmentation maps of different scales,perform mean filtering in the same superpixel label region of the segmentation map,and use support vector machine(SVM)classifier to classify the multi-scale superpixel segmentation images.A fusion decision strategy based on majority voting is adopted to obtain the final classification result.The experimental results show that the proposed method can realize an overall accuracy of 98.84%and average accuracy of 97.75%on the simulated mural multi-spectral image dataset.Hence,the proposed method can provide more accurate classification results than the control group.