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

Source: [Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification](<https://www.sota2.com/research/paper/68fa75f91e209131cea6abd9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:11.539Z

Authors: Anyu Zhang, Haotian Wu, Zeyu Cao

Published: 2022-03-14T11:07:33.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.07000>)

[PDF](<https://arxiv.org/pdf/2203.07000.pdf>)