Multi-dataset collaborative hyperspectral band selection based on evolutionary multitasking optimization

During this process, both intra-task interaction and inter-task interaction are introduced. The former enhances the search within the same task, while the latter enables knowledge transfer across different tasks, thereby facilitating the reuse of potentially effective band combinations and improving global search ability. For the private unsupervised tasks, information entropy is used as the objective function to preserve information-rich bands.

For the shared supervised task, a separability criterion based on between-class scatter and within-class scatter is adopted to characterize the discriminative quality of the selected bands more directly under limited labeled samples. After task optimization, EMDCBS obtains multiple diverse optimal band subsets from different tasks. Since these subsets emphasize different properties, a multiple band subset fusion strategy is further introduced.

By jointly considering information content, discriminative ability, and redundancy, the proposed strategy generates a final band subset with better comprehensive performance for each dataset.