Abstract
Subpixel mapping of hyperspectal image is treated in this paper, to produce a classification map in subpixel level. Specifically, a framework with two paralleled branches integrated by decision fusion is proposed. In one branch, a subpixel level segmentation map is obtained by applying unsupervised clustering to the upsampled hyperspectral image. In the other one, a subpixel mapping result is obtained by combining supervised classification, spectral unmixing and subpixel spatial attraction model. To improve the subpixel mapping accuracy, a labeled-unlabeled hybrid endmember library as well as optimized abundances are employed for spectral unmixing. Experimental results illustrate that the proposed approach clearly outperforms some state-of-the-art subpixel mapping approaches, and is less dependent on the supervised classifier adopted. The improvement can be attributed to the introduction of endmember library augmentation and the following abundance optimization.
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.