基于改进型相关向量机的高光谱图像分类

相关向量机(RVM)高光谱图像分类算法是一种基于贝叶斯概率模型的监督机器学习算法,其分类精度较高、测试时间较短。然而算法本身存在训练时间随着训练样本增加直线上升、分类效率整体降低等问题。针对这种情况,提出一种基于改进型相关向量机(VRVM)的高光谱图像分类算法。本算法在传统概率模型中引入一个新的分布,使得计算复杂度较高的积分运算可近似地拆分成两个较为简单的对数和形式。实验结果表明,VRVM高光谱图像分类算法的总体分类精度和相关向量的数量与RVM基本相同,但训练时间随样本数的增加有明显的减少。

Hyperspectral Image Classification Based on Variational Relevance Vector Machine

The hyperspectral image classification algorithm of relevance vector machine (RVM) is a supervised machine learning algorithm based on Bayes probability model, whose classification accuracy is good and the test time is short. However, the traditional RVM has some shortcomings that the training time will be very long and the effectiveness of the algorithm might decrease if the size of training samples is big or the dimensionality of the data is high. To solve these problems, a hyperspectral image classification algorithm of variational relevance vector machine (VRVM) is proposed. A new distribution is imported into the traditional probability model, which can replace complicated convolution operation with simple logarithm addition operation. Experimental results show that, in the classification of hyperspectral image, the overall classification accuracy and the number of relevance vectors of VRVM are nearly the same with RVM. However, with the increase of the sample, the training time has obviously reduced.