Multi-source collaborative domain generalization for cross-scene remote sensing image classification

Han, Zhu; Zhang, Ce ORCID: https://orcid.org/0000-0001-5100-3584; Gao, Lianru; Zeng, Zhiqiang; Ng, Michael K.; Zhang, Bing; Chanussot, Jocelyn. 2024 Multi-source collaborative domain generalization for cross-scene remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2024.3478385

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Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1109/TGRS.2024.3478385
UKCEH and CEH Sections/Science Areas:	UKCEH Fellows
ISSN:	0196-2892
Additional Keywords:	image Classification, domain generalization, cross-scene, remote sensing, multi-source data
NORA Subject Terms:	Electronics, Engineering and Technology Data and Information
Date made live:	24 Oct 2024 12:20 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538294

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