Spectral denoising in hyperspectral imaging using the discrete wavelet transform
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.
Keywords
HSI, spectral denoising, wavelet transform, hyperspectral analysis
Author Biography
Rafael Iván Rincón-Fonseca
Ingeniero Mecatrónico
Carlos Alberto Velásquez-Hernández
Ingeniero Mecatrónico, Magíster en Ingeniería
Flavio Augusto Prieto-Ortiz
Ingeniero Electrónico, Doctor en Automática Industrial
References
- Bjorgan, A., & Randeberg, L. L. (2015). Real-time noise removal for line-scanning hyperspectral devices using a Minimum Noise Fraction-based approach. Sensors, 15 (2), 3362-3378. https://doi.org/10.3390/s150203362
- Chen, G. Y., Bui, T. D., & Krzyżak, A. (2005). Image denoising with neighbour dependency and customized wavelet and threshold. Pattern Recognition, 38, 115-124.
- Chen, Y., Li, J., & Zhou, Y. (2020). Hyperspectral image denoising by total variation-regularized bilinear factorization. Signal Processing, 174, id 107645. https://doi.org/10.1016/j.sigpro.2020.107645
- Dos Santos Netoa, J. P., Dantas de Assisb, M. W., Parkutz Casagrandea, I., Cunha Júniorc, L. C., & de Almeida Teixeiraa, G. H. (2017). Determination of ‘Palmer’ mango maturity indices using portable near infrared (VIS-NIR) spectrometer. Postharvest Biology and Technology, 130, 75-80.
- Fan, H., Li, J., Yuan, Q., Liu, X., & Ng, M. (2019). Hyperspectral image denoising with bilinear low rank matrix factorization. Signal Processing, 163, 132-152. https://doi.org/10.1016/j.sigpro.2019.04.029
- Farzam, M., & Baheshti, S. (2011). Information Theoretic assessment of correlated noise in hyperspectral signal unmixing. 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE).
- Heylen, R., Burazerovic, D., & Scheunders, P. (2011). Constrained Least Squares Spectral Unmixing by Simplex Projection. IEEE Transactions on Geoscience and Remote Sensing, 49 (11), 4112-4122.
- Karami, A., Heylen, R., & Scheunders, P. (2014). Hyperspectral image noise reduction and its effect on spectral unmixing. 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).
- Liao, W., Aelterman, J., Luong, H. Q., Pižurica, A., & Philips, W. (2013). Two-stage denoising method for hyperspectral images combining KPCA and total variation. Proceedings of the 20th IEEE International Conference on Image Processing (ICIP '13), 2048–2052.
- Munera, S., Besada, C., Aleixos, N., Talens, P., Salvador, A., Sun, D., Cubero, S., & Blasco, J. (2017). Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT, 77, 241-248.
- Navrozidis, I., Alexandridis, T., Dimitrakos, A., Lagopodi, A., Moshou, D., & Zalidis, G. (2018). Identification of purple spot disease on asparagus crops across spatial and spectral scales. Computers and Electronics in Agriculture, 148, 322-329. https://doi.org/10.1016/j.compag.2018.03.035
- Pinto, J., Rueda-Chacón, H., & Arguello, H. (2019). Classification of Hass avocado (persea americana mill) in terms of its ripening via hyperspectral images. TecnoLógicas, 22 (45), 109-128.
- Vélez-Rivera, N., Gómez-Sanchis, J., Chanona-Pérez, J., Carrasco, J., Millán-Giraldo, M., Lorente, D., Cubero, S., & Blasco, J. (2014). Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosystems Engineering, 122, 91-98. https://doi.org/10.1016/J.BIOSYSTEMSENG.2014.03.009
- Yuan, Q., Zhang, Q., Li, J., Shen, H., & Zhang, L. (2019). Hyperspectral Image Denoising Employing a Spatial–Spectral Deep Residual Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 57 (2), 1205-1218. https://doi.org/10.1109/TGRS.2018.2865197
- Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., Hernández-Clemente, R., Kattenborn, T., Montes-Borrego, M., Susca, L., Morelli, M., Gonzalez-Dugo, V., North, P. R. J., Landa, B. B., Boscia, D., Saponari, M., & Navas-Cortes, J. A. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4, 432–439.
- Zelinski, A. C., & Goyal, V. K. (2014). A Novel approach to hyperspectral bands election based on spectral shape similarity analysis and fast branch and bound search. Engineering Applications of Artificial Intelligence, 27, 241–250.