Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification

TL;DR

This paper introduces a multi-source collaborative domain generalization framework for remote sensing image classification, leveraging adversarial augmentation and feature diversification to improve cross-scene generalization amid large domain shifts.

Contribution

The paper proposes a novel MS-CDG framework that combines data-aware adversarial augmentation with model-aware diversification for enhanced domain generalization in remote sensing.

Findings

Outperforms state-of-the-art methods on three remote sensing datasets.

Effectively handles large domain shifts with improved accuracy.

Enhances model robustness through diversified feature learning.

Abstract

Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions and reduce hand-crafted cost in the field of remote sensing. However, existing approaches focus on single-source domain generalization to unseen target domains, and are easily confused by large real-world domain shifts due to the limited training information and insufficient diversity modeling capacity. To address this gap, we propose a novel multi-source collaborative domain generalization framework (MS-CDG) based on homogeneity and heterogeneity characteristics of multi-source remote sensing data, which considers data-aware adversarial augmentation and model-aware multi-level diversification simultaneously to enhance cross-scene generalization performance. The data-aware adversarial augmentation adopts an adversary neural network with semantic guide to generate MS samples by adaptively learning realistic channel and distribution changes across domains. In views of cross-domain and intra-domain modeling, the model-aware diversification transforms the shared spatial-channel features of MS data into the class-wise prototype and kernel mixture module, to address domain discrepancies and cluster different classes effectively. Finally, the joint classification of original and augmented MS samples is employed by introducing a distribution consistency alignment to increase model diversity and ensure better domain-invariant representation learning. Extensive experiments on three public MS remote sensing datasets demonstrate the superior performance of the proposed method when benchmarked with the state-of-the-art methods.