Geospatial Data Analytics Laboratory, The Ohio State University, Columbus, OH 43210, USA.
Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH 43210, USA.
Sensors (Basel). 2023 Jan 28;23(3):1437. doi: 10.3390/s23031437.
Hyperspectral imaging is capable of capturing information beyond conventional RGB cameras; therefore, several applications of this have been found, such as material identification and spectral analysis. However, similar to many camera systems, most of the existing hyperspectral cameras are still passive imaging systems. Such systems require an external light source to illuminate the objects, to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting and the imaging system cannot function in a dark or low-light environment. This work develops a prototype system for active hyperspectral imaging, which actively emits diverse single-wavelength light rays at a specific frequency when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is more standardized to extract reflectance information; second, the system is capable of focusing on the desired spectral range by adjusting the number and type of LEDs; third, an active system could be mechanically easier to manufacture, since it does not require complex band filters as used in passive systems. Three lab experiments show that such a design is feasible and could yield informative hyperspectral images in low light or dark environments: (1) spectral analysis: this system's hyperspectral images improve food ripening and stone type discernibility over RGB images; (2) interpretability: this system's hyperspectral images improve machine learning accuracy. Therefore, it can potentially benefit the academic and industry segments, such as geochemistry, earth science, subsurface energy, and mining.
高光谱成像是能够捕捉超出传统 RGB 相机信息的技术；因此，已经发现了许多应用，例如材料识别和光谱分析。然而，与许多相机系统类似，大多数现有的高光谱相机仍然是被动成像系统。此类系统需要外部光源来照亮物体，以捕获光谱强度。因此，采集的图像高度依赖于环境照明，并且成像系统无法在黑暗或低光照环境下工作。本工作开发了一种主动高光谱成像的原型系统，该系统在成像时主动发射特定频率的各种单波长光线。这个概念有几个优点：首先，使用受控照明，各个波段的强度更加标准化，以提取反射率信息；其次，系统能够通过调整 LED 的数量和类型来聚焦于所需的光谱范围；第三，主动系统在机械制造上可能更容易，因为它不需要像被动系统那样使用复杂的带通滤波器。三个实验室实验表明，这种设计是可行的，并且可以在低光照或黑暗环境中生成有信息的高光谱图像：（1）光谱分析：与 RGB 图像相比，该系统的高光谱图像提高了食物成熟度和石材类型的可识别性；（2）可解释性：该系统的高光谱图像提高了机器学习的准确性。因此，它可能有益于学术和工业领域，例如地球化学、地球科学、地下能源和采矿。
