高光谱遥感图像为地物的精确分类带来了机遇，但也面临着一些挑战，高光谱遥感图像分类中所面临的一个挑战是如何处理高的光谱维数和小的样本数目之间的矛盾，目前几乎全部采用降维方法来缓解这一矛盾。集成学习的出现和选择性集成概念的提出为解决这一问题提供了新的研究思路，基于这一思想提出了基于波段分组和分类器集成的方法。在高光谱遥感图像的原始光谱空间根据波段之间的相似性信息对光谱波段进行分类，从每类中随机抽取一个波段形成新的光谱组，并依靠限制不同光谱组中相同波段的数目增加不同光谱组之间的差异程度，将新的光谱组作为训练分类器的特征子集，在特征子集训练最大似然分类器，使用简单的多数投票法合成得到最终的集成分类器。实验结果表明，使用基于波段分组和分类器集成的方法可以得到更高的分类精度。

The conflict of high dimensionality and the very limited number of available training samples is one of the problems in the classification of hyperspectral images. At the same time, the redundance between different bands brings trouble to the classification. The ensemble learning provides a new way for solving the problem mentioned above. Based on the correlation between different bands, a band grouping is carried out. By selecting different bands from different groups new subsets of spectral bands is formed. The redundance reduces bands in the new spectral band subsets are independent and used to train the maximum likelihood (ML) classifiers which can be used later for ensembling. The combining of classifiers is done by the simple majority voting and the ensemble classifier is formed. Experimental results of the hyperspectral remotely sensing image demonstrate that the method presented here has an excellent classification result and outpeforms many other methods.