Automated image post-processing system for multispectral imaging polarimeter: A review of current state
Heading:
| 1Manziuk, DYu., 2Syniavskyi, II, 1Bezuglyi, MO 1National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine 2Main Astronomical Observatory of the National Academy of Sciences of Ukraine, Kyiv, Ukraine |
| Space Sci. & Technol. 2025, 31 ;(6):049-062 |
| https://doi.org/10.15407/knit2025.06.049 |
| Publication Language: English |
Abstract: This article presents a review of the current state of automated post-processing for images captured by multispectral polarimeters. It analyzes key methods and algorithms used for calibration, segmentation, and data classification. The study demonstrates that combining multispectral and polarimetric information provides a deeper understanding of object and environment properties compared to using a single modality alone. Special attention is given to modern software tools for processing polarization images, highlighting their capabilities and limitations across various application domains. The growing role of machine learning and artificial intelligence methods is emphasized, as they enable efficient automation of large-scale data analysis. The importance of high-quality postprocessing is underscored, including georeferencing of image sensor pixels, calculation of geometric parameters, and correction of instrumental errors. The article also explores the potential for integrating post-processing results with GRASP (Generalized Retrieval of Atmosphere and Surface Properties) software to improve the accuracy of aerosol and cloud property retrieval.
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| Keywords: artificial intelligence, automated post-processing, calibration, classification, GRASP, multispectral polarimetry, remote sensing, segmentation |
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