Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification
Journal:
arXiv
Published Date:
Dec 5, 2024
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.
