Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss
<|reference_start|>Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss: In this study, we tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution, meticulously curated through several field expeditions across a mountainous region in France covering various landscapes. The task of plant species identification is framed as a semantic segmentation problem for its practical and efficient implementation across vast geographical areas. However, when dealing with segmentation masks, we confront instances where distinguishing boundaries between plant species and their background is challenging. We tackle this issue by introducing a fuzzy loss within the segmentation model. Instead of utilizing one-hot encoded ground truth (GT), our model incorporates Gaussian filter refined GT, introducing stochasticity during training. First experimental results obtained on both our UHR dataset and a public dataset are presented, showing the relevance of the proposed methodology, as well as the need for future improvement.<|reference_end|>
@article{pande2024plant,
    title={Plant detection from ultra high resolution remote sensing images: A
  Semantic Segmentation approach based on fuzzy loss},
    author={Shivam Pande, Baki Uzun, Florent Guiotte, Thomas Corpetti, Florian
  Delerue, S'ebastien Lef`evre},
    journal={arXiv preprint arXiv:2409.00513},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00513},
    primaryClass={cs.CV cs.AI}
}