Work

Dynamic fusion of medium-resolution optical and SAR imagery for methane source infrastructure classification

Year: 2025

Type: article

Abstract: Accurate classification of methane source infrastructure across sectors is critical for building comprehensive emission inventories and tracing emission sources. Existing approaches predominantly rely on high-resolution remote sensing imagery to capture discriminative features, but their scalability is limited by high costs and restricted availability. In contrast, medium-resolution imagery offers scalable alternatives with enhanced spectral signatures, while its lower spatial resolution challenges precise characterization and facility differentiation. To address this issue, we propose a multimodal fusion method on Sentinel-2 and Sentinel-1 data, with the aim of exploiting the complementary characteristics of optical, infrared, and SAR imagery to improve classification accuracy. We present a multimodal dynamic fusion network (DMFNet), which incorporates a gating module and multimodal attention fusion modules (MAFM) to adaptively address sample variability and multimodal heterogeneity. Additionally, DMFNet enables tracking and interpreting the fusion process by analyzing data-driven weights, providing deep insights into modality combinations and fusion strategies for specific facility. Experiments on the METER-ML dataset demonstrate that the proposed model achieves a precision of 0.740 and a recall of 0.757, outperforming existing single-modal and static fusion methods. Transferability experiments further confirm the practical applicability of the proposed method and its complementarity with existing open-source data in improving methane emission inventories.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104876