用自监督学习从树的高光谱图像中提取更优表征，提升农业分析效果
Self-supervised Learning for Hyperspectral Images of Trees
- 构建与植被属性相关的嵌入空间，实现无标签高光谱图像表征
- 新表征在下游任务中性能优于直接使用原始光谱数据
- 适合农业遥感、植被监测等无标注场景应用
利用多光谱和RGB影像进行航空遥感已为精准农业提供关键支持。对缺乏或没有标签的高光谱图像进行分析极具挑战。本文聚焦于自监督学习，旨在从农田航拍高光谱图像中生成反映树木植被特性的神经网络嵌入表征。实验表明，基于植被属性相关嵌入空间构建的树木表征，在下游机器学习任务中表现优于直接使用高光谱植被属性作为表征的方法。
原文摘要 · Abstract (English)
Aerial remote sensing using multispectral and RGB imagers has provided a critical impetus to precision agriculture. Analysis of the hyperspectral images with limited or no labels is challenging. This paper focuses on self-supervised learning to create neural network embeddings reflecting vegetation properties of trees from aerial hyperspectral images of crop fields. Experimental results demonstrate that a constructed tree representation, using a vegetation property-related embedding space, performs better in downstream machine learning tasks compared to the direct use of hyperspectral vegetation properties as tree representations.
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