Optimal band selection for hyperspectral data with improved differential evolution

The optimal band selection has been a hot research topic and one of the difficult problems in the field of hyperspectral remote sensing. To address the issue of hyperspectral data and improve the data processing speed, we propose a new method to improve differential evolution algorithm. We use the ENVI software on the pretreatment of original spectral data and dividing subspace. The experiments are also conducted with the improved DE algorithm. In experiments, different evaluation criteria of band selection are taken as the fitness function value of the DE algorithm. By calculating the optimal band combination in different dimensions of size, we can obtain the optimal selection band of hyperspectral data in this paper. Experiments show that the improved differential evolution algorithm for hyperspectral data dimensionality reduction model not only obtains the optimal band combination, but also greatly improves the classification accuracy.