An overview of pre-processing methods available for hyperspectral imaging applications

Hyperspectral imaging techniques have emerged as a powerful tool, combining the benefits of vibrational spectroscopy and imaging into a single system. By capturing both spatial and spectral information, hyperspectral imaging offers valuable insights into the characteristics of a sample. However, prior to the application of hyperspectral imaging techniques, it is crucial to perform pre-processing on the acquired images. The integration of vibrational spectroscopy and imaging in hyperspectral imaging enables researchers to obtain detailed information about the chemical composition and spatial distribution of samples. With this technology, researchers have delved into various applications, ranging from pharmaceutical analysis, food, and agricultural assessment to environmental monitoring. However, to ensure the accuracy and reliability of the results, appropriate preprocessing techniques are essential. Pre-processing methods play a vital role in reducing or eliminating interferences that may arise during image acquisition and subsequent analysis. These interferences could be attributed to various factors, such as noise, uneven illumination, or unwanted artifacts. By applying suitable preprocessing techniques, researchers can enhance the quality of hyperspectral images, ensuring more accurate and reliable data for further analysis. This review aims to provide an overview of the pre-processing techniques employed in the analysis of hyperspectral images.

Automated summary

Hyperspectral imaging combines vibrational spectroscopy and imaging to provide detailed information about the chemical composition and spatial distribution of samples. Pre-processing techniques are essential to remove interferences and enhance the quality of hyperspectral images.