Abstract

The very high resolution (VHR) images can be seen as multiview data. For better organizing and highlighting similarities and differences between the multiple views of data, a semisupervised multiview feature selection (SemiMFS) method is proposed in this paper, based on consensus and complementary principles. In SemiMFS, feature views are generated by decomposing features into multiple disjoint and meaningful groups. Each feature group represents a view, and each view describes a data characteristic. Then features are evaluated and selected within each view. The experiments on a Worldview-2 VHR satellite image verify the effectiveness and practicability of the method, compared with traditional single-view algorithms.

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 call

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.