Hyperspectral remote sensing data have a wealth of spectral information that can describe objects in detail. However

redundancy occurs due to the high correlation of adjacent bands in the narrow-band continuous spectrum space. This redundancy leads to high computational complexity and dimension disaster in data analysis. As an important means of dimension reduction

band selection can reduce these negative effects. The goal of band selection is to retain the relevant information needed in practical applications with as few bands as possible. Therefore

two aspects of discussion are involved: selection criteria and selection methods (search methods). A new band search method based on band subspace is proposed to improve band searching efficiency. This method needs to input the required number of bands without any other parameter settings. First

the spectral space of hyperspectral data is partitioned according to the block characteristics of band correlation coefficient matrix image and the adjacent transitive correlation

which is partitioned as a first subspace. Then

on the basis of the actual demand band number

the first subspace is divided secondary according to the proportion of the subspace size

and the final band subspace is obtained. Second

a band is selected according to certain rules (e.g.

the maximum standard deviation) in each final band subspace to form an initial band subset. Lastly

after the objective function (e.g.

the average correlation of the band subsets

the best index

the overall classification accuracy) is set

bands are replaced by each subspace to increase (or decrease) the value of objective function until no replacement can improve the goal further

which is the final band subset we pursued. The other three band selection methods are compared with our method on two opened hyperspectral data to verify the validity of the proposed method. Experimental results show that as a fast search strategy

the computational time of the proposed method is much less than the exhaustive band combination. The proposed method has faster search efficiency and convergence than the artificial bee colony algorithm for all kinds of objective functions. Moreover

compared with band selection method based on spectral clustering and adaptive band selection

this method can flexibly transform the target function for specific applications and obtain more suitable band subsets for different requirements. The correlation of spectral space of hyperspectral data is flexibly used in this study. It substantially reduces the computational complexity of the search algorithm in the band selection that combined the subspace partition with band search. Moreover

few parameters are used to simplify the complexity of the model and reduce the time spent in parameter tuning. For different application requirements

the proposed method flexibly transforms the objective function so that the searched bands’ combination becomes suitable for these requirements.