高光谱遥感图像的高数据维给图像进一步处理带来了困难，为了解决这一问题，提出了自适应波段选择(ABS)的降维方法。该方法充分考虑了高光谱图像的空间相关性和谱间相关性，通过计算各个波段的指数来选择信息量大并且与其他波段相关性小的波段。对各波段相应的指数重新排列之后，有两种方法来选择最终波段：一种是选择波段指数比设定指数大的波段，另一种方法是选择波段指数排在前n个的所有波段。为了验证ABS方法的有效性，对降维后的高光谱图像进行了贝叶斯监督分类，分类结果表明自适应波段选择的方法能够选择出信息丰富的波段，分类精度与使用原始波段相比提高10．4％，计算复杂度大大降低。

The high dimensions of hyperspectral remote sensing image have caused problems to further processing. In order to solve the above problems, this paper proposes an adaptive band selection (ABS) algorithm of dimensional reduction, which selects high informative and low correlative bands by calculating the index of each band. After calculating and rearranging the index of each band, there are two methods of selecting the final bands: one is to select the bands whose index is bigger than the specified index, another is to select the first n bands whose index is the first n bands. In order to testify the effect of ABS method, Bayesian supervised classification was implemented on the dimensionally reduced image. The results of the classification show that highly informative bands can be selected by ABS method, the classification accuracy of selected bands is 10 4% bigger than the original image, and the computing complication is decreased rapidly too.