As the adjacent bands of a hyperspectral image are highly correlated, it is not optimum to classify the hyperspectral image in the high dimensional space. To solve the problem, a novel hyperspectral image classifier based on Steepest Ascent and Relevance Vector Machine (SA-RVM) was proposed in this paper. The SA was used to search an optimum feature space and to eliminate redundant features of the image firstly. Then, RVM was trained in the optimized feature subspace and used to classify the test samples. Experiments were performed for four sets of data,it is shown that the accuracies of RVM have raised more than 2. 5% in the feature subspace selected by SA, which is close to those of Support Vector Machines(SVMs). For the two data sets with fewer training samples,the accuracies of RVM increase by 5. 63% and by 6. 2% in the subspace. In addition, benefiting from the sparse solution,the SA-RVM requires very short time in predicting the class labels of unknown sam ples. It concludes that the SA - RVM has higher precision and efficiency in the prediction , and it is suitable for processing the large-scale hyperspectral images.%针对高光谱影像近邻波段高度相关,直接在高维空间分类并非最优的问题,提出了基于最速上升和关联向量机(SA-RVM)的高光谱影像分类算法.使用最速上升(SA)算法搜索最优特征子空间,剔除冗余特征；然后,在特征子空间中训练RVM并分类.对4套测试数据进行的实验表明,SA选择的特征子空间中,RVM分类精度提高了2.5％以上,与支持向量机(SVM)相当.对训练样本较少的2套数据,精度提高了5.63％和6.2％.此外,SA-RVM的解稀疏,预测未知样本类别属性所需时间短.总体来看,SA-RVM精度高、判别速度快,适合处理大场景高光谱影像.
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