Multiple Data Sets Collaborative Analysis for Hyperspectral Band Selection

Traditional band selection methods only analyze one data set at a time, and start searching band subsets from the zero ground state of knowledge, which can not effectively mine spectral information to guide band selection. However, for hyperspectral images obtained by the same sensor, the spectral information has 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 paper, a multiple data sets collaborative analysis framework for hyperspectral band selection is proposed to realize spectral information communication, thereby guiding and promoting band selection of each data set. Different band selection tasks are established pertinently, 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 data sets 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 data set analysis.