Particle swarm optimization/impurity function class overlapping scheme based on multiple attribute decision making model for hyperspectral band selection

This paper presents a promising band selection algorithm, known as particle swarm optimization/impurity function class overlapping (PSO/IFCO) method, which adopts a novel multiple attribute decision making (MADM) model approach to the hyperspectral remote sensing images. The proposed MADM-based PSO/IFCO method can be divided into two steps: 1) PSO algorithm and 2) the IFCO scheme. With PSO band selection algorithm, the highly correlated bands of hyperspectral imagery can first be grouped into band modules, known as greedy modular eigenspace (GME), to coarsely reduce high-dimensional datasets in the first step. The more highly correlated small modules are further constructed with the statistics of impurity weights calculated by IFCO scheme in the second step. These statistics results of impurity weights are used to finely select the most important feature bands from the hyperspectral imagery. The proposed MADM-based PSO/IFCO makes use of the correlation coefficients matrix to cluster the highly correlated bands together and obtain GME in the first step. More specifically, we use the analytic hierarchy process (AHP) model, which is the most suitable implementation of MCDM for proposed method, to examine hierarchically the relations among different GME modules with the impurity weighted by IFCO in the second step. Finally, by accommodating the statistics of impurity weights, the proposed MADM-based PSO/IFCO method can effectively select the most representative features for hyperspectral band selection and reduction. The effectiveness of the proposed method is evaluated by MASTER and AVIRIS hyperspectral images. The experimental results demonstrate that the proposed method can not only enhance the high dimension reduction rate, but also offer a satisfactory classification performance.