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A heuristic approach to learning new graph structures for remote sensing images
conference contribution
posted on 2023-05-23, 11:51 authored by Brian SalmonBrian Salmon, Kleynhans, W, Jan OlivierJan Olivier, Schwegmann, CPA probability graph model can effectively model spectral and spatial dependencies within remote sensing images for land cover classification. The most common structure used to unify this probabilistic information is a second order Markov network that encapsulate unary and pairwise potentials. In this paper we explore various heuristics to discover new graph structures that will assist with classifying land cover. Experiments were conducted to compare classification accuracies in two study areas; one homogeneous and one heterogeneous located in the Kwazulu-Natal province, South Africa.
History
Publication title
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)Pagination
3051-3054ISBN
978-1-5090-3332-4Department/School
School of EngineeringPublisher
Institute of Electrical and Electronics EngineersPlace of publication
United States of AmericaEvent title
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)Event Venue
Beijing, ChinaDate of Event (Start Date)
2016-07-10Date of Event (End Date)
2016-07-15Rights statement
Copyright 2016 IEEERepository Status
- Restricted