Abstract
A competitive neural network (NN) learned by the OLVQ1 algorithm has the potential to produce images for land cover mapping. We propose an extended OLVQ1 that adds learning ratios to the original algorithm. Applying it to SPOT XS data, we show that higher mapping accuracies can be obtained compared to those of conventional methods.
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 callMore From: IEEE Transactions on Geoscience and Remote Sensing
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.
