Lopes Tomás, Capela Diana, Guimarães Diana, Ferreira Miguel F S, Jorge Pedro A S, Silva Nuno A
INESC TEC, Center for Applied Photonics, 4169-007, Porto, Portugal.
Departamento de Física, Faculdade de Ciências da Universidade do Porto, 4169-007, Porto, Portugal.
Sci Rep. 2024 Apr 20;14(1):9123. doi: 10.1038/s41598-024-59553-y.
Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.
多模态光谱成像提供了一种独特的方法，通过结合从不同来源收集的信息来增强独立光谱技术的分析能力。在本手稿中，我们通过聚焦于两种著名的光谱成像技术，即激光诱导击穿光谱和高光谱成像，来探索此类机会，并针对一个涉及矿物识别的案例研究探索协同传感的机会。具体而言，这项工作基于两种不同的方法：一种是传统的传感器融合，我们努力通过纳入来自两种模态的信息来增加收集到的信息；另一种是知识蒸馏方法，其中激光诱导击穿光谱被用作高光谱成像的自主监督器。我们的结果显示了这两种方法在提高单模态传感系统性能方面的潜力，尤其突出了知识蒸馏框架在最大化使用多种技术构建更具可解释性模型的潜在益处以及为工业应用铺平道路方面的优势。
