Multiobjective Optimization-Based Hyperspectral Band Selection for Target Detection

Band selection can reduce information redundancy and improve application efficiency of hyperspectral images (HSIs), such as classification. Traditional band selection methods usually weight different objectives and combine them together in a single function, making it difficult to balance conflict between various criteria. Recently, multiobjective optimization band selection (MOBS) techniques have got a lot of attention, which evaluate bands from different aspects separately and search for a solution well-balancing all the objectives simultaneously. However, few algorithms are focused on target detection, and most of them use just two objectives, which are not sufficient enough to select optimal band subset for detection. To alleviate these problems, this article proposes an algorithm named target-oriented multiobjective optimization of band selection (TOMOBS). First, the multiobjective optimization (MO) framework with three objective functions is modeled, involving information, noise, and correlation of the bands, respectively. Second, to optimize the proposed model, the noninferior solution advantage matrix (NAM) is designed based on the swarm intelligence optimization method, which can provide accurate solutions and improve the descriptiveness of MO problems (MOPs). Third, a target-oriented evaluation mechanism is developed to guide selecting final result from the Pareto front (PF), especially designed for target detection. Experiments on real hyperspectral datasets show that this algorithm can provide a subset of bands with strong representational capability for target detection and achieve impressing results compared with the state-of-the-art methods.