Abstract
Neural networks have received much attention in the field of remote sensing. Topology identification remains however one of the major difficulties in the efficient application of neural networks. Currently, topology determination is based on trial and error, on heuristics that amalgamate past experience and on weight pruning algorithms. It is argued in this paper that global search methods such as genetic algorithms can be deployed in discovering near optimal network topologies. An example on multisource classification for land cover mapping is presented. The results indicate that the global search paradigm is worth further exploration especially now that computing becomes more and more powerful.
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.
