Hyper-Spectral Image Pixel Classification Based on Golden Sine and Chaotic Spotted Hyena Optimization Algorithm

Hyperspectral remote sensing technology is a breakthrough technology that integrates imaging and spectral methods, but it also faces issues such as classification accuracy being controlled by dimensionality and spectral variability. Based on this, this study proposes a Golden Sine Spotted Hyena Optimization (GSSHO) algorithm that integrates the Golden Sine method, chaos strategy, and tournament strategy to help hyperspectral remote sensing technology achieve higher work efficiency. The implementation of the algorithm can be roughly divided into three stages. This includes initializing using chaotic strategies, calculating the individual fitness value and selecting the best cluster, and after updating the fitness value, continuing to select the optimal solution until the stop condition of the algorithm is met. Among them, the golden sine algorithm provides a new data update method for the spotted hyena optimization algorithm, avoiding situations where individual data cannot be searched for. The Salinas and Pavian Centre datasets were used in the experiment to validate the effectiveness of the improved spotted hyena optimization algorithm. At the same time, four other common algorithms were selected for performance comparison experiments with the algorithm. The experimental results show that the GSSHO algorithm has excellent dimensionality reduction and convergence capabilities. Compared with the total number of original bands, in both datasets, the two values decreased to the total of 32 and 11, respectively, equivalent to 1/6 and 1/9 of the original data; The fitness function values are 0.0836 and 0.0315 respectively. And compared with the other four algorithms, this algorithm also significantly outperforms other algorithms in all aspects of indicators. Therefore, the spotted hyena optimization algorithm based on the golden sine algorithm and chaotic strategy can make band selection more efficient.