中文摘要：

提出一种独立分量分析（ICA）和相关向量机（RVM）相结合的高光谱数据分类方法，首先采用虚拟维数方法对高光谱数据维数进行估计，在此基础上，采用独立分量分析对数据进行降维，然后采用相关向量机对降维后的数据分类。计算机仿真实验结果表明，该方法在获得较高分类精度的同时大大节省了分类时间。

英文摘要：

A hyperspectral data classification method of combining the independent component analysis（ICA） and relevance vector machine（RVM） is put forward. A method named virtual dimension（VD） is introduced to estimate the dimension of hyperspectral data. On this basis, ICA is used to reduce the dimension, and then RVM is used to classify the data whose dimension has been reduced. The computer simulation results show that the method achieves a high accuracy classification and greatly reduce the classification time.