联合超像素主成分分析降维和扩展随机游走概率优化的高光谱图像分类

Hyperspectral Image Classification Combining Superpixel Principal Component Analysis Dimensionality Reduction with Extended Random Walk Probability Optimization

针对高光谱图像分类中小规模训练样本下空间信息利用不足和分类精度下降问题,提出一种联合超像素降维和类别后验概率优化的高光谱图像分类方法。首先根据高光谱图像的空间纹理结构,采用熵率超像素分割算法自适应地识别均匀同质超像素区域,对每个区域逐一应用主成分分析,挖掘能表征图像空间-光谱信息的超像素混合特征;然后将混合特征输入支持向量机中计算各像元初始类别概率向量,采用扩展随机游走算法利用图像空间邻域信息对初始类别进行后验概率优化;最后根据各像元最大类别概率确定分类结果。在Indian Pines、Pavia University和Salinas等3组通用高光谱数据集上开展实验,与其他6种方法进行对比,实验结果表明:在有限训练样本条件下,所提方法的总体分类精度分别为98.29%、97.29%和99.72%,优于对比方法的分类结果。

A hyperspectral image classification method based on joint hyperpixel dimension reduction and category posterior probability optimization is proposed to address the issues of inadequate utilization of spatial information and decline in classification accuracy under small-and medium-sized training samples.First,the entropy rate hyperpixel segmentation algorithm,based on the spatial texture structure of hyperspectral images was used to adaptively detect homogeneous hyperpixel regions,and principal component analysis was applied to each region individually to mine the hyperpixel mixture features that can represent the spatial-spectral information of images.Then,the initial category probability vector of each pixel was calculated using the mixed features provided in the support vector machine,and the extended random walk algorithm was used to optimize the initial category using the image space neighborhood information.Finally,the classification result was calculated based on the maximum classification probability of each pixel.Experiments were performed on three general hyperspectral datasets,including Indian Pines,Pavia University,and Salinas,and compared with the other six methods.Even with a small number of training samples,the experimental results show that the proposed method’s overall classification accuracy is 98.29%,97.29%,and 99.72%,respectively,which is superior to the classification results of the comparison methods.