Evolving U-Nets Using Genetic Programming for Tree Crown Segmentation

Created by W.Langdon from gp-bibliography.bib Revision:1.7970

@InProceedings{fu:2023:IaVC,

• author = "Wenlong Fu and Bing Xue and Mengjie Zhang and Jan Schindler",
• title = "Evolving {U-Nets} Using Genetic Programming for Tree Crown Segmentation",
• booktitle = "37th International Conference, Image and Vision Computing, IVCNZ 2022",
• year = "2022",
• editor = "Wei Qi Yan and Minh Nguyen and Martin Stommel",
• volume = "13836",
• series = "LNCS",
• pages = "188--201",
• address = "Auckland, New Zealand",
• month = nov # " 24-25",
• publisher = "Springer",
• note = "Revised Selected Papers",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-031-25824-4",
• URL = "http://link.springer.com/chapter/10.1007/978-3-031-25825-1_14",
• DOI = "doi:10.1007/978-3-031-25825-1_14",
• abstract = "The U-Net deep learning algorithm and its variants have been developed for biomedical image segmentation, and due to their success gained popularity in other science domains including remote sensing. So far no U-Net structure has been specifically designed to segment complex tree canopies from aerial imagery. In this paper, a handcrafted convolutional block is introduced to replace the raw convolutional block used in the standard U-Net structure. Furthermore, we proposed a Genetic Programming (GP) approach to evolving convolutional blocks used in the U-Net structure. The experimental results on a tree crown dataset show that both the handcrafted block and the GP evolved blocks have better segmentation results than the standard U-Net. Additionally, the U-Net using the proposed handcrafted blocks has fewer numbers of the learning parameters than the standard U-Net. Also, the proposed GP approach can evolve convolutional blocks used in U-Nets that perform better than the handcrafted U-Net and the standard U-Net, and can also achieve automation.",
• notes = "Published in 2023 after the conference",

}

Genetic Programming entries for Wenlong Fu Bing Xue Mengjie Zhang Jan Schindler

Citations