用多光谱与热成像融合检测水泥厂热污染，准确率达90.6%
FusionNet: Physics-Aware Representation Learning for Multi-Spectral and Thermal Data via Trainable Signal-Processing Priors
- 融合热红外与短波红外数据，嵌入可学习信号处理先验
- 在五种光谱配置下最高达90.6%准确率，优于现有模型1.1个百分点
- 强调跨模态训练重要性，适合工业环境监测应用
水泥生产支撑全球基建但贡献约7%人为二氧化碳排放，精准监测对可持续发展至关重要。现有遥感方法主要依赖窑炉热信号，易受背景热源干扰，且无法捕捉持续工业热影响导致的土壤属性变化。本研究提出FusionNet，一种融合多光谱与热红外数据的物理感知框架，利用地质短波红外（SWIR）比值特征检测工业热排放引发的土壤变化。该模型通过专用主干网络整合TIR与SWIR输入，将差分信号处理先验嵌入卷积层、混合池化与更大感受野设计。系统消融实验表明各组件均有增益，DGCNN相较传统CNN提升4.1-6.8%准确率。在SWIR比值数据集上，FusionNet最高达90.6%，超越五种光谱配置下的先进基线模型，并超出最强单模态模型1.1%。迁移学习实验显示，ImageNet预训练会降低TIR与SWIR性能，凸显跨光谱任务中模态感知训练的重要性。结果表明，结合物理引导特征选择与合理深度学习架构，可实现高鲁棒性、高精度水泥厂检测，为工业基础设施监测提供可靠方案。
原文摘要 · Abstract (English)
Cement production underpins global infrastructure but contributes approximately 7% of anthropogenic CO2 emissions, making accurate monitoring of production facilities essential for sustainable development. Existing remote sensing approaches rely predominantly on thermal signatures from kiln operations, which can be confounded by background heat sources and fail to capture persistent environmental alterations. This study introduces a physics-informed methodology that exploits multi-spectral features, particularly a geological Short Wave Infrared (SWIR) ratio, to detect soil property changes induced by sustained industrial heat emissions. This work proposes FusionNet, an intermediate multi-spectral data fusion framework that integrates Thermal Infrared (TIR) and SWIR inputs through a specialised backbone, embedding differential signal processing priors within a convolutional layer, mixed pooling, and wider receptive field. Systematic ablation studies confirm that each architectural component contributes to performance gains, with DGCNN achieving a 4.1-6.8% accuracy improvement over conventional CNNs. On the SWIR ratio dataset, FusionNet attains a maximum of 90.6%, outperforming state-of-the-art baselines across five spectral configurations and exceeding the strongest unimodal model by 1.1%. Transfer learning experiments reveal that ImageNet pretraining degrades TIR and SWIR performance, underscoring the importance of modality-aware training for cross-spectral applications. Overall, the results demonstrate that combining physics-aware feature selection with principled deep learning architectures enables robust, high-accuracy detection of cement production facilities, offering a reliable framework for industrial infrastructure monitoring
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