Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, EDYTEM, 73000 Chambéry, France; Univ. Savoie Mont Blanc, LISTIC, 74000 Annecy, France.
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, EDYTEM, 73000 Chambéry, France.
Sci Total Environ. 2022 Apr 15;817:152018. doi: 10.1016/j.scitotenv.2021.152018. Epub 2021 Nov 29.
Hyperspectral imaging (HSI) is a non-destructive, high-resolution imaging technique that is currently under significant development for analyzing geological areas with remote devices or natural samples in a laboratory. In both cases, the hyperspectral image provides several sedimentary structures that must be separated to temporally and spatially describe the sample. Sediment sequences are composed of successive deposits (strata, homogenite, flood) that are visible depending on sample properties. The classical methods to identify them are time-consuming, have a low spatial resolution (millimeters) and are generally based on naked-eye counting. In this study, we compare several supervised classification algorithms to discriminate sedimentological structures in lake sediments. Instantaneous events in lake sediments are generally linked to extreme geodynamical events (e.g., floods, earthquakes), so their identification and counting are essential to understand long-term fluctuations and improve hazard assessments. Identification and counting are done by reconstructing a chronicle of event layer occurrence, including estimation of deposit thicknesses. Here, we applied two hyperspectral imaging sensors (Visible Near-Infrared, VNIR, 60 μm, 400-1000 nm; Short Wave Infrared, SWIR, 200 μm, 1000-2500 nm) on three sediment cores from different lake systems. We highlight that the SWIR sensor is the optimal one for creating robust classification models with discriminant analyses (prediction accuracies of 0.87-0.98). Indeed, the VNIR sensor is impacted by the surface reliefs and structures that are not in the learning set, which causes mis-classification. These observations are also valid for the combined sensor (VNIR-SWIR) and the RGB images. Several spatial and spectral pre-processing were also compared and enabled one to highlight discriminant information specific to a sample and a sensor. These works show that the combined use of hyperspectral imaging and machine learning improves the characterization of sedimentary structures compared to conventional methods.
高光谱成像（HSI）是一种非破坏性的、高分辨率的成像技术，目前正在大力开发，用于分析远程设备或实验室中自然样本的地质区域。在这两种情况下，高光谱图像都提供了几个必须分离的沉积结构，以便对样本进行时间和空间描述。沉积序列由连续的沉积物（地层、均质体、洪水）组成，这些沉积物取决于样本的性质而可见。识别它们的经典方法既耗时，空间分辨率又低（毫米级），通常基于肉眼计数。在这项研究中，我们比较了几种监督分类算法，以区分湖泊沉积物中的沉积结构。湖泊沉积物中的瞬时事件通常与极端地球动力学事件（如洪水、地震）有关，因此识别和计数这些事件对于理解长期波动和提高灾害评估至关重要。识别和计数是通过重建事件层发生的编年史来完成的，包括估计沉积物的厚度。在这里，我们在三个来自不同湖泊系统的沉积物核心上应用了两种高光谱成像传感器（可见近红外，VNIR，60μm，400-1000nm；短波红外，SWIR，200μm，1000-2500nm）。我们强调，SWIR 传感器是创建具有判别分析（预测精度为 0.87-0.98）的稳健分类模型的最佳传感器。事实上，VNIR 传感器受到不在学习集中的表面起伏和结构的影响，这会导致错误分类。这些观察结果也适用于组合传感器（VNIR-SWIR）和 RGB 图像。我们还比较了几种空间和光谱预处理方法，这些方法能够突出特定于样本和传感器的判别信息。这些工作表明，与传统方法相比，高光谱成像和机器学习的组合使用可以提高对沉积结构的特征描述。
