Particle swarm optimization-based impurity function band prioritization using weighted majority voting for feature extraction of high dimensional data sets

In recent years, with the improvement of sensor technologies, the volumes of remote sensing data are increased dramatically. The feature extraction of hyper spectral remotely sensed images can reduce such high-dimensional datasets, solve the big data problem, avoid the Hughes phenomena and improve the classification performance. Accordingly, this paper presents a framework for feature extraction of hyper spectral imagery, which consists of two approaches, referred to as parallel particle swarm optimization (PPSO) band selection and weighted voting impurity function (WVIF) band prioritization. The highly correlated bands of hyper spectral imagery can be grouped first into the some modules by PPSO band selection algorithm to coarsely reduce high-dimensional datasets, and these highly correlated band modules can then be analyzed with the statistical relationship between bands and classes by WVIF band prioritization method to finely select the most important feature bands form the datasets. Furthermore, a PPSO algorithm based on modern graphics processing unit (GPU) architecture using NVIDIA compute unified device architecture (CUDA) technology is using in this paper. It can improve the computational speed of PPSO band selection to group the high correlated band modules. The effectiveness of the proposed PPSO/WVIF framework is evaluated by MASTER and AVIRIS hyper spectral images. The experimental results demonstrated that the proposed method not only could reduction the dimension of datasets, but also can offer a satisfactory classification performance and computational speed.