Attention-Based Multisource Remote Sensing Fusion Using Hyperspectral and LiDAR Observations
Keywords:
attention mechanisms; hyperspectral imaging; LiDAR; multisource fusion; remote sensing infrastructure; socio-technical systems; deep learning governanceAbstract
The integration of hyperspectral imaging and Light Detection and Ranging observations represents a transformative capability in modern remote sensing, enabling simultaneous acquisition of high-dimensional spectral signatures and precise three-dimensional structural information. While conventional fusion methods have relied primarily on stacking feature vectors or applying linear transformations, these approaches often fail to capture the complex, non-linear interactions between spectral and spatial modalities. This paper presents a comprehensive analysis of attention-based architectures for multisource remote sensing fusion, examining their capacity to model long-range dependencies, prioritize salient features, and dynamically weight contributions from heterogeneous data streams. We systematically explore the architectural trade-offs associated with attention mechanisms, including self-attention, cross-attention, and multi-head configurations, within the context of hyperspectral and LiDAR data fusion. The study further investigates the implications of such systems for large-scale infrastructure deployment, emphasizing considerations of computational efficiency, energy consumption, data governance, and model robustness across varying geographic and operational conditions. Through a cross-domain analytical lens, we examine how attention-based fusion frameworks can be designed to support sustainable and equitable deployment in environmental monitoring, urban planning, and disaster response. The paper also addresses challenges related to spectral redundancy, spatial resolution disparities, and label scarcity, proposing governance-oriented strategies for training data curation and model validation. By situating technical innovations within broader socio-technical infrastructure systems, the analysis underscores the need for transparent, auditable, and fair fusion methodologies. We conclude that attention-based multisource fusion, when informed by rigorous structural and policy considerations, offers a robust pathway toward more accurate, resilient, and interpretable remote sensing systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
