Evolving a Cloud-Robust Water Index with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{batista:2022:GECCOlba,

• author = "Joao Batista and Sara Silva",
• title = "Evolving a {Cloud-Robust} Water Index with Genetic Programming",
• booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion",
• year = "2022",
• editor = "Yew-Soon Ong and Abhishek Gupta",
• pages = "55--56",
• address = "Boston, USA",
• series = "GECCO '22",
• month = "9-13 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, feature construction, water indices, remote sensing, satellite imagery",
• isbn13 = "978-1-4503-9268-6/22/07",
• DOI = "doi:10.1145/3520304.3533946",
• abstract = "Over the years, remote sensing (RS) experts created many indices to help them study satellite imagery by highlighting characteristics like vegetation, water, or burnt areas, among others. In this work, we study water indices. Although there is a large number of water indices that work perfectly in unclouded imagery, clouds and shadows cast by clouds are often mistaken for water. This work is focused on the automatic feature construction using genetic programming (GP), in an attempt to make features that are more robust to these issues. To do this, we use a dataset containing pixels from areas where we could find these issues to evolve models that learn how to classify those pixels correctly. The results indicate improvements when comparing evolved features with indices, but further improvements are required to tackle other issues found.",
• notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Joao E Batista Sara Silva

Citations