Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification

TL;DR

This paper introduces a self-supervised contrastive learning approach for hyperspectral image classification that constructs diverse views via cross-channel prediction and achieves state-of-the-art unsupervised classification performance.

Contribution

It proposes a novel cross-view construction method using cross-channel prediction and applies contrastive learning to hyperspectral data, improving unsupervised classification accuracy.

Findings

Achieves state-of-the-art unsupervised classification results

Utilizes four cross-channel-prediction augmentation methods

Effective in learning semantically consistent features

Abstract

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.