LAUSR

Traditional band selection methods only analyze one dataset at a time and start searching band subsets from the zero ground state of knowledge, which cannot effectively mine spectral information to guide band selection. However, for hyperspectral images (HSIs) obtained by the same sensor, the spectral information has a similar physical meaning (radiance or reflectivity). Collaborative analysis technology can analyze multiple hyperspectral datasets to explore the inherent spectral features shared among them. In this letter, a multiple datasets collaborative analysis framework for hyperspectral band selection is proposed to realize spectral information communication, thereby guiding and promoting the band selection of each dataset. Different band selection tasks are established pertinently, and then, the evolutionary multitasking band selection method is designed to facilitate the knowledge sharing of different band selection tasks. More importantly, the interaction mechanism among different datasets is adjusted dynamically, thereby improving the cooperation ability of the collaborative analysis framework. Besides, a predominant gene reservation crossover and a deduplication mutation are designed for retaining the promising bands and avoiding the selection of repeat bands. Experiments indicate that the proposed collaborative analysis method works more efficiently than the comparison methods and successfully enhances accuracy and convergence compared to single dataset analysis.