Open AccessSpectral denoising in hyperspectral imaging using the discrete wavelet transform
Author(s) -
Rafael Iván Rincón-Fonseca,
Carlos Alberto Velásquez-Hernández,
Flavio Prieto
Publication year - 2021
Publication title -
revista de investigación, desarrollo e innovación/revista de investigación desarrollo e innovación
Language(s) - English
Resource type - Journals
eISSN - 2389-9417
pISSN - 2027-8306
DOI - 10.19053/20278306.v11.n3.2021.13359
Subject(s) - hyperspectral imaging , noise reduction , noise (video) , artificial intelligence , pattern recognition (psychology) , computer science , discrete wavelet transform , wavelet , wavelet transform , mathematics , computer vision , image (mathematics)
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.