Abstract
This study deals with two different approaches to the classification of hyperspectral image data using a multiple stage classifier structured as a binary tree. One approach implements the Gaussian maximum likelihood (GML) decision function at each node of the tree and the second makes use of traditional binary logistic regression (LR). The results obtained by classification of AVIRIS images data are compared with single- stage classifiers.
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.