Band selection is an important dimensionality reduction (DR) methodology for hyperspectral images . Many ranking-based clustering band selection methods have been developed. However, these methods do not consider the combination of bands in different clusters but only select the desired number of clustering centers based on band ranking to construct the reduced band subset, which may lead to obtaining a set of bands with low redundancy but little information or a set of bands with a large amount of information but high redundancy, thus falling into the local optimal solution set. To solve this problem, an unsupervised hybrid hyperspectral band selection method is proposed in this paper. In this article, an inherent connection between bad band removal and target detection has been found.As we know, the result of target detection is the linear combination of all bands, and the weight coefficient of each band, i.e., the component of the filter vector, can be considered as the contribution of each band for the detection of targets of interest. Based on this fact, we develop an automatic bad band pre-removal method by using the matched filter.Then comes the progressive band selection processing of hyperspectral image classification, which performs classification in multiple stages in the sense that each stage performs Hyperspectral image classification progressively according to a specifically selected band subset.For each level we are getting best bands through band optimization technique.At last classifiers are used upon Indian pines dataset which gives us the best band selection.