Abstract
The use of hyperspectral sensors has gained relevance in agriculture due to its potential in the phytosanitary management of crops. However, these sensors are sensitive to spectral noise, which makes their real application difficult. Therefore, this work focused on the analysis of the spectral noise present in a bank of 180 hyperspectral images of mango leaves acquired in the laboratory, and the implementation of a denoising technique based on the discrete wavelet transform. The noise analysis consisted in the identification of the highest noisy bands, while the performance of the technique was based on the PSNR and SNR metrics. As a result, it was determined that the spectral noise was present at the ends of the spectrum (417-421nm and 969-994nm) and that the Neigh-Shrink method achieved a SNR of the order of 1011 with respect to the order of 102 of the original spectrum.
Highlights
Introduction techniques and approaches to solving the problem of noise detection and removal in as in any sensors and measuring m susceptible to the presence of noise in both conclusions
Each group composed of 5 samples was pho
The problem of additive denoising in iminto: ber of bands that such an image can contain ponent can correspond to the background of
Summary
Introduction techniques and approaches to solving the problem of noise detection and removal in as in any sensors and measuring m susceptible to the presence of noise in both conclusions. ORCID: https://orcid.org/0000-0002-1053-3148 edu.co m m m u unal.edu.co
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