Abstract
We propose an improved semi-supervised local discriminant analysis (ISELD) for feature extraction of hyperspectral image in this paper. The proposed ISELD method aims to find a projection which can preserve local neighborhood information and maximize the class discrimination of the data. Compared to the previous SELD, the proposed ISELD better models the correlation of labeled and unlabeled samples. Experimental results on an ROSIS urban hyperspectral image are encouraging. Compared to some recent feature extraction methods, our approach has more than 2% improvements as the training sample size changes.
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.
