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4 December 2024 Simulation experiment on classification and recognition of several typical airport runway foreign objects based on NIR hyperspectral imaging
Shenlan Tang, Qingsong Liu, Shuai Wang, Youquan Dan, Kaige Li, Luopeng Xu, Hua Wu, Mengdi Li
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Proceedings Volume 13283, Conference on Spectral Technology and Applications (CSTA 2024); 132833U (2024) https://doi.org/10.1117/12.3037177
Event: Conference on Spectral Technology and Applications (CSTA 2024), 2024, Dalian, China
Abstract
Foreign object debris (FOD) on airport runways is an important factor affecting aircraft flight safety, and current FOD detection technologies all have obvious deficiencies. In this paper, an indoor near-infrared (NIR) hyperspectral image data acquisition system with a wavelength range of 900-1700nm was built. The 14 samples of 6 common FODs and airport concrete runways were divided into reference and test sample sets, and the atlas data were collected for two common application scenarios. Preprocessing was performed on the reference sample set of hyperspectral images and reference spectral curves were extracted for 7 types of samples. Six spectral matching algorithms based on spectral angle matching (SAM), spectral information divergence (SID), spectral correlation coefficient (SCC) and their combinations are used to classify pixels one by one. By comparing the classification map, overall accuracy (OA), average accuracy (AA), and Kappa coefficient, a NIR hyperspectral FOD detection method based on SAM-SID (threshold Sc=40 pixel) criterion is obtained. The proposed method obtained ideal classification maps for the test sample set, with OA, AA and Kappa coefficients reaching 92%, 82% and 0.82, respectively, thus achieving good validation.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shenlan Tang, Qingsong Liu, Shuai Wang, Youquan Dan, Kaige Li, Luopeng Xu, Hua Wu, and Mengdi Li "Simulation experiment on classification and recognition of several typical airport runway foreign objects based on NIR hyperspectral imaging", Proc. SPIE 13283, Conference on Spectral Technology and Applications (CSTA 2024), 132833U (4 December 2024); https://doi.org/10.1117/12.3037177
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KEYWORDS
Hyperspectral imaging
Image classification
Near infrared
Data acquisition
Statistical analysis
Glasses
Hyperspectral simulation
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