论文标题
跨视图预测：探索高光谱图像分类的对比功能
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification
论文作者
论文摘要
本文提出了一种自我监督的特征学习方法，用于高光谱图像分类。我们的方法试图通过交叉代理学习方法构建原始高光谱图像的两个不同观点。然后通过对比度学习方法学习对创建观点的语义一致表示。具体而言，自然设计了四种基于跨通道预测的增强方法，以利用高光谱数据的高维度来进行视图结构。通过从我们的对比网络的不同视图中最大化相互信息并最大程度地减少有条件的熵，可以学到更好的代表性特征。这种“跨视图”风格很简单，并通过简单的SVM分类器获得了无监督分类的最新性能。
This paper presents a self-supervised feature learning method for hyperspectral image classification. Our method tries to construct two different views of the raw hyperspectral image through a cross-representation learning method. And then to learn semantically consistent representation over the created views by contrastive learning method. Specifically, four cross-channel-prediction based augmentation methods are naturally designed to utilize the high dimension characteristic of hyperspectral data for the view construction. And the better representative features are learned by maximizing mutual information and minimizing conditional entropy across different views from our contrastive network. This 'Cross-View-Predicton' style is straightforward and gets the state-of-the-art performance of unsupervised classification with a simple SVM classifier.
