Abstract
Hyperspectral images can provide spectral characteristics related to the physical properties of different materials, which arouses great interest in many fields. Band selection (BS) could effectively solve the problem of high dimensions and redundant information of HSI data. However, most BS methods utilize a single measurement criterion to evaluate band importance so that the assessment of bands is not comprehensive. To dispose of these issues, we propose the hypergraph spectral clustering band selection (HSCBS) method in this letter. First, a novel hypergraph construction method is proposed to combine bands selected by different priority criteria. Second, based on the hypergraph Laplacian matrix, an unsupervised band selection model named HSCBS is presented to cluster the bands into compact clusters with high within-class similarity and low between-class similarity. The results of comprehensive experimental on two public real datasets demonstrate the effectiveness of HSCBS.
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 callDisclaimer: 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.
