Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification
This work addresses the problem of hyperspectral image classification for remote sensing applications, presenting an incremental improvement in unsupervised feature learning.
The paper tackles hyperspectral image classification by proposing a self-supervised method that constructs two views of the data using cross-channel-prediction augmentations and learns features via contrastive learning, achieving state-of-the-art performance in unsupervised classification with an SVM classifier.
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.