Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model

TL;DR

This paper introduces MIFOMO, a foundation model-based approach for cross-domain few-shot hyperspectral image classification, leveraging domain adaptation and label smoothing to outperform prior methods.

Contribution

It proposes a novel foundation model framework with coalescent projection and mixup domain adaptation for improved CDFSL in hyperspectral imaging.

Findings

MIFOMO outperforms previous methods by up to 14% in accuracy.

The approach effectively addresses domain discrepancy and noisy labels.

Source code is publicly available at the provided GitHub link.

Abstract

Although cross-domain few-shot learning (CDFSL) for hyper-spectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealistic data augmentation procedure in the form of external noise to enlarge the sample size, thus greatly simplifying the issue of data scarcity. They involve a large number of parameters for model updates, being prone to the overfitting problem. To the best of our knowledge, none has explored the strength of the foundation model, having strong generalization power to be quickly adapted to downstream tasks. This paper proposes the MIxup FOundation MOdel (MIFOMO) for CDFSL of HSI classifications. MIFOMO is built upon the concept of a remote sensing (RS) foundation model, pre-trained across a large scale of RS problems, thus featuring generalizable features. The notion of coalescent projection (CP) is introduced to quickly adapt the foundation model to downstream tasks while freezing the backbone network. The concept of mixup domain adaptation (MDM) is proposed to address the extreme domain discrepancy problem. Last but not least, the label smoothing concept is implemented to cope with noisy pseudo-label problems. Our rigorous experiments demonstrate the advantage of MIFOMO, where it beats prior arts with up to 14% margin. The source code of MIFOMO is open-sourced at https://github.com/Naeem-Paeedeh/MIFOMO for reproducibility and convenient further study.