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

• URL: http://arxiv.org/abs/2412.03897v1
• Date: Thu, 05 Dec 2024 06:15:08 GMT
• Title: Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification
• Authors: Zhu Han, Ce Zhang, Lianru Gao, Zhiqiang Zeng, Michael K. Ng, Bing Zhang, Jocelyn Chanussot,
• Abstract summary: Cross-scene image classification aims to transfer prior knowledge of ground materials to annotate regions with different distributions. Existing approaches focus on single-source domain generalization to unseen target domains. We propose a novel multi-source collaborative domain generalization framework (MS-CDG) based on homogeneity and heterogeneity characteristics of multi-source remote sensing data.
• Score: 57.945437355714155
• License:
• 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.

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