Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Ambient Intelligence and Humanized Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
