Abstract
Recently, the dictionary-aided sparse regression (SR) method for hyperspectral unmixing has received much attention in the field of remote sensing. However, under the assumption that each pixel in the hyperspectral scene can be viewed as a combination of endmembers in the spectral library, most of SR methods ignore the spectral signature mismatches between an actual spectral signature and its corresponding endmember in spectral library. To overcome this problem, we proposed a joint optimizing unmixing model called DSPCSR which includes dictionary sparse pruning and collaborative sparse regression. By exploiting the sparse property of spectral mismatch error and the collaborative sparse property of the abundance matrix, the DSPCSR can provide good robustness and performance. Experiments on the synthetic and real datasets show that the proposed DSPCSR can achieve better performance compared with several state-of-art algorithms.
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.