Summary of Plant Detection From Ultra High Resolution Remote Sensing Images: a Semantic Segmentation Approach Based on Fuzzy Loss, by Shivam Pande et al.
Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss
by Shivam Pande, Baki Uzun, Florent Guiotte, Thomas Corpetti, Florian Delerue, Sébastien Lefèvre
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study addresses the challenge of identifying plant species from ultra-high resolution remote sensing images. The approach involves creating an RGB remote sensing dataset with millimeter-level spatial resolution, carefully curated through field expeditions across various landscapes in France. The task is framed as a semantic segmentation problem for efficient implementation over large areas. However, distinguishing boundaries between plant species and their background can be challenging due to the complexity of the images. To tackle this issue, the study introduces a fuzzy loss within the segmentation model. Instead of using one-hot encoded ground truth, the model incorporates Gaussian filter refined ground truth, introducing stochasticity during training. The proposed methodology is tested on both the UHR dataset and a public dataset, demonstrating its relevance and highlighting the need for future improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us better understand how to use super-high-tech cameras in the sky to figure out what kinds of plants are growing on the ground. To do this, researchers created a special collection of images with really detailed information about different types of landscapes and plants. They used computers to try to identify which plants were in each picture, but it’s hard because sometimes the lines between different plants get blurry. To solve this problem, they came up with a new way for the computer to learn from mistakes. This method seems to work pretty well, but there’s still room for improvement. |
Keywords
» Artificial intelligence » One hot » Semantic segmentation
