Abstract
Multitemporal domain adaptation (DA) is very useful for solving the spectral drift problem between different images and is a basis step of multitemporal classification. However, for high-resolution images, they always have a few spectral bands. A few spectral bands are difficult to establish accurate alignment model. In order to achieving accurate multitemporal alignment on a few spectral bands’ high-resolution images, source label learning step is proposed in this letter and used to optimize traditional manifold alignment (MA). The core of this method is to improve the erroneous manifold structure by combining majority voting and weighting coefficients. Besides, this method is a universal step and can be used for optimizing all MA methods. Two groups of data sets captured by Chinese GF1 and GF2 satellites are used for performance evaluation. The experimental results demonstrate the effectiveness of our method and indicate our method significantly outperforms the traditional DA methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.