论文标题
自我监督学习的几次高光谱图像分类
Self Supervised Learning for Few Shot Hyperspectral Image Classification
论文作者
论文摘要
事实证明，深度学习是高光谱图像（HSI）分类的一种非常有效的方法。但是，深度神经网络需要大量注释的数据集来概括地概括。这限制了深度学习对HSI分类的适用性，在该分类中，每个场景的数千个像素手动标记都是不切实际的。在本文中，我们建议利用自我监督学习（SSL）进行HSI分类。我们表明，通过使用Barlow-Twins（一种最先进的SSL算法）在未标记的像素上预先培训编码器，我们可以获得具有少数标签的准确模型。实验结果表明，这种方法的表现明显优于香草的监督学习。
Deep learning has proven to be a very effective approach for Hyperspectral Image (HSI) classification. However, deep neural networks require large annotated datasets to generalize well. This limits the applicability of deep learning for HSI classification, where manually labelling thousands of pixels for every scene is impractical. In this paper, we propose to leverage Self Supervised Learning (SSL) for HSI classification. We show that by pre-training an encoder on unlabeled pixels using Barlow-Twins, a state-of-the-art SSL algorithm, we can obtain accurate models with a handful of labels. Experimental results demonstrate that this approach significantly outperforms vanilla supervised learning.
