Dimensionality reduction is considered to be a primary task for effective classification of hyperspectral images. In this work, a novel feature (band) selection technique based on rough set theory is presented to reduce the dimensionality of hyperspectral images. Here, a new definition of dependency measure in rough set theory is proposed by not only considering the objects in the positive region but also some objects from the boundary region. The proposed dependency definition is completely parameter free and computationally very cheap. Our technique, first, defines a novel criterion by combining the relevance and significance measure computed using the proposed dependency definition. Then, a first-order incremental search is adopted to select the most informative bands by maximizing the defined criterion. The proposed band selection technique shows better result compared to the existing rough set based band selection techniques on three real hyperspectral data sets.

降维被认为是对高光谱图像进行有效分类的主要任务。在这项工作中，提出了一种基于粗糙集理论的新颖特征（波段）选择技术，以降低高光谱图像的维数。在此，不仅考虑正区域中的对象，而且考虑边界区域中的一些对象，提出了粗糙集理论中依赖度量的新定义。所提出的依赖项定义完全没有参数，并且计算上非常便宜。首先，我们的技术通过结合使用建议的依赖项定义计算的相关性和显着性度量来定义一种新颖的标准。然后，通过最大化定义的标准，采用一阶增量搜索来选择信息量最大的波段。