With the development of hyperspectral imaging technology

the raising spectral resolution improves the ability of target detection and classification.But its great data size and high data dimension also bring challenge to analysis and processing.As a dimensionality reduction technology

band selection (BS) plays an important role in the pre-processing of hyperspectral imagery (HSI).However

few BS algorithms are specially designed for target detection.In this paper

based on analyzing the character of constrained energy minimization (CEM) algorithm

a sparse representation based band selection method (TD-SRBBS) is proposed for HSI target detection.The symmetric Kullback-Leibler divergence is defined for subspatial partition

which makes the original HSI dataset some subset.Sparse reconstruct the detection result in each subset

and then band selection can be implemented based on the one-to-one correspondence between selected bands and nonzero elements of sparse vector.The experiments on real hyperspectral data demonstrate the effectiveness of TD-SRBBS.