554 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 14, NO. 4, APRIL 2017 Particle Swarm Optimization-Based Band Selection for Hyperspectral Target Detection Yan Xu, Student Member, IEEE, Qian Du, Senior Member, IEEE, and Nicolas H. Younan, Senior Member, IEEE Abstract— This letter proposes particle swarm optimization (PSO)-based band selection (BS) approach for hyperspectral target detection. Due to lack of training samples in a detection problem, it is more difficult than classification-purposed BS. The objective function, called maximum-submaximum-ratio (MSR) gauging target-background separation, is proposed for target detection during PSO searching. Typical target detectors such as target-constrained interference-minimized filter and adaptive coherence estimator are studied. Experimental results demon- strate that the proposed MSR-based objective function in con- junction with PSO-based searching can select a small band set while yielding similar or even better detection performance than using all the original bands, sequential forward search-based BS, or BS relying on detection map similarity assessment. Index Terms— Band selection (BS), hyperspectral imagery, particle swarm optimization (PSO), target detection. I. I NTRODUCTION T ARGET detection is one of major tasks of hyperspectral imaging. A hyperspectral image cube contains hundreds of spectral bands, leading to a high computation burden on target detection. Thus, it is necessary to do dimensional reduc- tion on the original data for efficient analysis. Typically, there are two kinds of methods for dimensional reduction. The first one is transform-based methods (such as principal component analysis [1]), and such methods may not be preferred since they alter the physical meaning of the data. The second one is band selection (BS), which is to select a subset of bands while still generating satisfactory results using the selected bands [2]–[4]. BS algorithms have been applied to hyperspectral data analysis, and they can be implemented in either unsupervised or supervised. Unsupervised BS is to select the subset of bands without any prior information. For instance, a simple yet efficient similarity measure method was developed in [5] for BS. Supervised BS algorithms intend to select the bands producing maximum class separability with prior knowledge of training samples. Many methods (see [5], [6]) have been developed to measure the class separability for BS. However, most BS algorithms are focused on classification problems, and few have been proposed for target detection. It is more difficult to separate targets and nontargets with selected bands Manuscript received September 27, 2016; revised December 31, 2016; accepted January 20, 2017. Date of publication February 22, 2017; date of current version March 3, 2017. The authors are with the Department of Electrical and Computer Engi- neering, Mississippi State University, MS 39762 USA (e-mail: yx131@ msstate.edu; du@ece.msstate.edu; younan@ece.msstate.edu). Color versions of one or more of the figures in this letter are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/LGRS.2017.2658666 due to lack of training samples for target and background modeling. The search strategy is also an important issue. To avoid exhaustive search, which is computationally prohibitive to hyperspectral BS, sequential forward search (SFS) and sequen- tial floating forward (SFFS) methods can be used [7]. Their basic idea is to select the best band for maximizing an objective function, then one additional band combining with the existing selected band or bands is selected to maxi- mize the objective function. The process continues until the desired number of bands is reached. Recently, particle swarm optimization (PSO), invented by Eberhard and Kennedy [10], has been applied to hyperspectral BS with the objective of maintaining classification accuracy [8], [9]. The PSO imitates the social behavior of flocks. Each particle is a solution, and the positions of particles are randomly initialized. Then the particles fly over the problem space until certain criteria are reached. Compared with SFS and SFFS, PSO offers two major advantages: it can provide better high-dimensional solution due to global search, and it can be easily implemented in parallel. Although several other evolutionary algorithms are developed recently, such as ant colony optimization [11] and firefly algorithm [12], which have been applied to hyperspec- tral BS, we limit our discussion with PSO in this letter. Here, we focus on the task of target detection where targets with known spectral signatures are to be detected from an unknown background. Targets, as small man-made objects, are often sparsely populated [13]. No training or labeled samples are available for both target and background [14]. The key of PSO-based selection is to design an effective objective function during searching. More often, labeled samples are available when evaluating an objective function. However, it is impossible for target detection. Thus, a detection-specific objective function is required. Intuitively, we can compare the detection outputs for band searching with a certain criterion, such as Euclidean distance or correlation coefficient (CC). However, a large similarity does not necessarily mean satisfac- tory performance, because the number of target pixels is much smaller than background pixels and similarity assessment is dominated by background. Thus, we prefer the metric that can well gauge target and background separation. Specifi- cally, we propose the maximum-submaximum-ratio (MSR) to quantify such separation. The typical target detectors, i.e., target-constrained interference-minimized filter (TCIMF) and adaptive coherence estimator (ACE), are studied. Experimental results using the proposed MSR-based objective function in conjunction with PSO-based searching can select a small band set while yielding similar or even better detection performance than using all the original bands. The parameter setting of MSR is discussed based on the performance of detectors. 1545-598X © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.