M2Caps: learning multi-modal capsules of optical and SAR images for land cover classification

Land cover classification (LCC) is essential for monitoring land use and changes. This study examines the integration of optical (OPT) and synthetic aperture radar (SAR) images for precise LCC. The disparity between OPT and SAR images introduces challenges in fusing high-level semantic information and utilizing multi-scale features. To address these challenges, this paper proposes a novel multi-modal capsules model (M²Caps) incorporating multi-modal capsules learning and cascaded features fusion modules. The multi-modal capsules learning module models high-level semantic information and abstract relationships across diverse remote sensing images (RSIs) modalities as vectors, thereby facilitating the induction of joint multi-modal features with high discriminability and robustness. Subsequently, the cascaded features fusion module integrates various feature scales, concurrently processing deep multi modal features, shallow OPT features, and shallow SAR features at each layer. This approach ensures the precise characterization of both local details and global semantics. M²Caps outperformed state-of-the-art models, improving mean intersection over union (mIoU) by 2.86% – 12.9% on the WHU-OPT-SAR dataset and 3.91% – 12.3% on the GF-2 and GF-3 Pohang datasets, demonstrating its effectiveness in high-precision LCC in complex environments.