用模糊损失提升毫米级遥感图像中植物识别精度
Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss
- 引入高斯滤波修正的标签，训练时加入随机性
- 在自建和公开数据集上均实现更好分割效果
- 适合需要精准植物边界识别的生态监测场景
本研究针对超高清（UHR）遥感图像中的植物物种识别挑战，构建了一个具有毫米级空间分辨率的RGB遥感数据集，该数据集通过多次野外考察采集于法国山区多种地貌区域。将植物物种识别问题建模为语义分割任务，以实现在大范围地理区域内的高效应用。然而，在处理分割掩码时，植物物种与背景之间的边界区分仍具挑战。为此，本文在分割模型中引入模糊损失函数，不使用传统的one-hot编码真值标签，而是采用高斯滤波优化后的真值标签，训练过程中引入随机性。在自建的UHR数据集及一个公开数据集上的初步实验结果表明，该方法具有显著有效性，同时揭示了未来改进的必要性。
原文摘要 · Abstract (English)
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.
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