High spectral resolution of hyperspectral images in form of very narrow and constant within visible and infrared spectral ranges has brought the technology of remote hyperspectral measurement into spotlight in order to detect objects as well as earthly phenomena. In this field, most methods have been presented with the purpose of improving the accuracy of image classification and there have been scarce researches on target detection (TD). Supervised  TD  problem  can  be  considered  as  a  one-class classification problem between the target and non-target pixels using  training  data  from  the  target  class  only.  However, a spectral signature of the target sample obtained using field or laboratory measurements is the only training data available for TD.
A substantial number of bands lead to heavy computational costs along with Hughes Effect on hyperspectral image processing. Hence, in recent years much attention has been paid to reduction of computational complexity in the processing of hyperspectral images. In comparison to the classification  field,  few  studies  have  been  done  on  dimension  reduction  or  band  selection  for  target  detection  in  hyperspectral images. A chief reason behind this is the shortage or absence of training samples of the desired target in background images. In order to solve this problem, a method is introduced based on target simulation. Recently, target simulation method has been used for creating artificial sub-pixel targets on hyperspectral images in order to investigate the performance of sub-pixel target detection (STD) algorithms. But in this paper target simulation has been used as method for optimum band selection. In this method for STD several simulated training samples, created by means of target spectrum implantation in the image.
For optimal band selection after achieving sufficient implanted targets as training data, searching strategy in hyperspectral image space is of high account. Once simulated targets are created, optimal bands are selected via Particle Swarm Optimization (PSO) Algorithm. To make the optimization algorithms exploit well in search space, their cost functions must be well defined. So one of contributions of this study is defining a new cost function for optimization algorithms used for selecting optimal bands. In the next stage, based on the optimal bands selected, the Local ACE is applied on the image to obtain detection result. ACE algorithm, one of the most useful and common algorithms for detection of sub-pixel and full pixel targets. But local ACE is commonly used to detect sub-pixel targets in hyperspectral images. In this version of the ACE algorithm, instead of using the mean and covariance of the entire image, just the neighbouring window pixels are used. The output of this stage will be TD map that by applying a threshold in a detection map can determine whether the pixels are target or not. In order to evaluate and study the ability of the introduced algorithm, Target Detection Blind Test (TDBT) of Hymap dataset and Hyperion dataset from Botswana have been used. False alarm rate was used as touchstone to evaluate the results between the outputs of different methods. Compared to some PSO-based algorithms such as maximum-submaximum-ratio (MSR) and correlation coefficient (CC), an evolutionary method such as genetic algorithm (GA), and the use of full band, the proposed method was able to improve the results by 46% in all cases where it was possible to decrease false alarm for the searched target.