Wunsch Lennard, Hubold Martin, Nestler Rico, Notni Gunther
Group of Quality Assurance and Industrial Image Processing, Faculty of Mechanical Engineering, Technische Universität Ilmenau, Gustav-Kirchhoff-Platz 2, 98693 Ilmenau, Germany.
Fraunhofer Institute for Applied Optics and Precision Engineering IOF Jena, Albert-Einstein-Str. 7, 07745 Jena, Germany.
Sensors (Basel). 2024 Dec 14;24(24):7984. doi: 10.3390/s24247984.
Multispectral imaging (MSI) enables the acquisition of spatial and spectral image-based information in one process. Spectral scene information can be used to determine the characteristics of materials based on reflection or absorption and thus their material compositions. This work focuses on so-called multi aperture imaging, which enables a simultaneous capture (snapshot) of spectrally selective and spatially resolved scene information. There are some limiting factors for the spectral resolution when implementing this imaging principle, e.g., usable sensor resolutions and area, and required spatial scene resolution or optical complexity. Careful analysis is therefore needed for the specification of the multispectral system properties and its realisation. In this work we present a systematic approach for the application-related implementation of this kind of MSI. We focus on spectral system modeling, data analysis, and machine learning to build a universally usable multispectral loop to find the best sensor configuration. The approach presented is demonstrated and tested on the classification of waste, a typical application for multispectral imaging.
多光谱成像（MSI）能够在一个过程中获取基于空间和光谱图像的信息。光谱场景信息可用于根据反射或吸收来确定材料的特性，从而确定其材料成分。这项工作聚焦于所谓的多孔径成像，它能够同时捕获（快照）光谱选择性和空间分辨的场景信息。在实施这种成像原理时，存在一些影响光谱分辨率的限制因素，例如可用的传感器分辨率和面积、所需的空间场景分辨率或光学复杂性。因此，需要仔细分析来确定多光谱系统的特性及其实现方式。在这项工作中，我们提出了一种针对此类MSI的与应用相关的实现系统方法。我们专注于光谱系统建模、数据分析和机器学习，以构建一个通用的多光谱循环来找到最佳的传感器配置。所提出的方法在废物分类这一多光谱成像的典型应用中进行了演示和测试。
