# CauseHSI: Counterfactual-Augmented Domain Generalization for Hyperspectral Image Classification via Causal Disentanglement

## TL;DR

This paper introduces CauseHSI, a new framework for improving hyperspectral image classification by using causal reasoning to handle differences between scenes.

## Contribution

The novel contribution is a causality-based framework with counterfactual generation and causal disentanglement modules for domain generalization in HSI classification.

## Key findings

- CauseHSI outperforms existing domain generalization methods on benchmark datasets.
- The framework effectively separates invariant causal features from domain-specific biases.
- Experiments show improved robustness to unseen target scenes in remote sensing applications.

## Abstract

Cross-scene hyperspectral image (HSI) classification under single-source domain generalization (DG) is a crucial yet challenging task in remote sensing. The core difficulty lies in generalizing from a limited source domain to unseen target scenes. We formalize this through the causal theory, where different sensing scenes are viewed as distinct interventions on a shared physical system. This perspective reveals two fundamental obstacles: interventional distribution shifts arising from varying acquisition conditions, and confounding biases induced by spurious correlations driven by domain-specific factors. Taking the above considerations into account, we propose CauseHSI, a causality-inspired framework that offers new insights into cross-scene HSI classification. CauseHSI consists of two key components: a Counterfactual Generation Module (CGM) that perturbs domain-specific factors to generate diverse counterfactual variants, simulating cross-domain interventions while preserving semantic consistency, and a Causal Disentanglement Module (CDM) that separates invariant causal semantics from spurious correlations through structured constraints under a structural causal model, ultimately guiding the model to focus on domain-invariant and generalizable representations. By aligning model learning with causal principles, CauseHSI enhances robustness against domain shifts. Extensive experiments on the Pavia, Houston, and HyRANK datasets demonstrate that CauseHSI outperforms existing DG methods.