A new dimensionality reduction algorithm for hyperspectral image using evolutionary strategy

Reducing the redundancy of spectral information is an important technique in classification of hyperspectral image. The existing methods are classified into two categories: feature extraction and band selection. Compared with the feature extraction, the band selection method preserves most of the characteristics of the original data without losing valuable details. However, the choice of the effective band remains challenging, especially when considering the computational burden, which makes many enumerative methods infeasible. Recently, immune clonal strategy (ICS) has been applied to solve complex computation problems. The major advantages of algorithms based on ICS are that they are highly paralleled, distributed, adaptive, and self-organizing. Therefore, in this paper, we convert the band selection problem into an optimization issue and propose a new algorithm, ICS-based effective band selection (ICS-EBS), to select effective band combinations. Then, the selected bands are used in classification of hyperspectral image. We evaluated the proposed algorithm by using two data sets collected from the Washington DC Mall and Northwest Tippecanoe County. ICS-EBS was compared against one latest proposed band selection algorithm, interclass separability index Algorithm (ICSIA). We also compared the results with those achieved by other stochastic algorithms such as genetic algorithm (GA) and ant colony optimization (ACO). The experimental results indicate that our proposed algorithm outperforms ICSIA, GA-EBS, and ACO-EBS for hyperspectral image classification.