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

IF 8.6 Q1 REMOTE SENSING
Yanglangxing He , Xueliang Zhang , Pengfeng Xiao , Zhenshi Li , Dilxat Muhtar , Feng Gu , Binxiao Liu , Pengming Feng
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引用次数: 0

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.
中分辨率光学影像与SAR影像动态融合用于甲烷源基础设施分类
跨行业甲烷源基础设施的准确分类对于建立综合排放清单和追踪排放源至关重要。现有的方法主要依靠高分辨率遥感图像来捕获判别特征,但其可扩展性受到高成本和有限可用性的限制。相比之下,中等分辨率图像提供了具有增强光谱特征的可扩展替代方案,而其较低的空间分辨率则对精确表征和设施区分提出了挑战。为了解决这一问题,我们提出了一种基于Sentinel-2和Sentinel-1数据的多模态融合方法,旨在利用光学、红外和SAR图像的互补特性来提高分类精度。我们提出了一个多模态动态融合网络(DMFNet),它包含一个门控模块和多模态注意力融合模块(MAFM),以自适应地解决样本可变性和多模态异质性。此外,DMFNet可以通过分析数据驱动的权重来跟踪和解释融合过程,为特定设施的模态组合和融合策略提供深入的见解。在METER-ML数据集上的实验表明,该模型的准确率为0.740,召回率为0.757,优于现有的单模态和静态融合方法。可移植性实验进一步证实了该方法在改进甲烷排放清单方面的实用性及其与现有开源数据的互补性。
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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