HSIAO Framework in Feature Selection for Hyperspectral Remote Sensing Images Based on Jeffries-Matusita Distance

Hyperspectral remote sensing image feature selection enhances the efficiency of further applications by extracting band information. However, it is challenging to optimize and extract the spectral relationship information of the entire hyperspectral image using traditional methods and solidified spectral representation strategies. As a result, band selection often leads to locally optimal candidate solutions. For instance, when applied to downstream classification tasks, the selected bands typically exhibit issues such as poor information separability, high spectral correlation, and missing information. This paper proposes a new HSIAO_BS framework based on Jeffries-Matusita distance (JM) and an evolutionary algorithm to obtain an excellent subset of bands for hyperspectral remote sensing image feature selection addressing downstream classification tasks. The research problem is modeled as a solution space with effective inter-spectral relationship representation. The HSIAO_BS framework designs an adaptive band encoding mechanism and a feature relationship representation based on JM distance to construct this space. Additionally, the key optimized search method in HSIAO_BS is the improved HSIAO. This evolutionary algorithm combines differential crossover and attenuating mutation strategies to enhance the balance between global exploration and local exploitation capabilities, while also targeting to improve the preference for band selection. The reliability, validity, and stability of the HSIAO_BS framework are verified through a series of performance test experiments conducted on three hyperspectral remote sensing image datasets to support downstream classification tasks.