Thermal infrared hyperspectral image band selection based on dynamic metric-guided particle swarm optimization for quantitative analysis of potash

Potash plays an important role in agriculture and food security, so does the estimation of the content of substances in potash. Chemical methods for such estimation tasks are time-consuming, whereas thermal infrared hyperspectral (TIH) images provide us with an opportunity to do estimation efficiently. This paper hence applies the TIH technology to predict the content of various substances in potash. On the one hand, the TIH technology provides rich hyperspectral information for substance discovery and analysis. On the other hand, it introduces challenges of dimensionality and information redundancy, leading to potential decreased predicting accuracy. To solve the problem, as well as easing the burden of detection instruments in terms of band usage, band selection is critical. This paper proposes a dynamic metric-guided particle swarm optimization (DM-GPSO) method for band selection. It adaptively adjusts parameters to prevent local optima, avoiding the need for manual parameter tuning. Variety of band selection algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and improved PSO algorithms, are tested to extract and analyse the characteristic bands of potash. Experimental results demonstrate that the TIH technology combined with a proper band selection methodis effective in estimating the content of potash. The new DM-GPSO can select different combinations of bands, which lead to better and more stable prediction results. The analysis on the combinations of selected bands also reveals that picromerite and potassium chloride have similar intervals of characteristic bands, which can be used to guide the production process of potash in practice.