bsuirДоклады БГУИРDoklady BGUIR1729-76482708-0382БГУИРbsuir-1115Research ArticleЭЛЕКТРОНИКА, РАДИОФИЗИКА, РАДИОТЕХНИКА, ИНФОРМАТИКАELECTRONICS, RADIOPHYSICS, RADIOENGINEERING, INFORMATICSПАРАМЕТРИЧЕСКИ НАСТРАИВАЕМЫЙ АЛГОРИТМ СЖАТИЯ ГИПЕРСПЕКТРАЛЬНЫХ ДАННЫХ С ПРИМЕНЕНИЕМ ВЕЙВЛЕТ-РАЗЛОЖЕНИЯThe parametrically adjusted hyperspectral data compression algorithm based on wavelet decompositionПерцевД. Ю.PertsauD. Y.

junior researcher of R&D department

ДудкинА. А.DoudkinA. A.

Дудкин Александр Арсентьевич - д.т.н., профессор, заведующий лабораторией идентификации систем

220012, г. Минск, ул. Сурганова, 6

Doudkin Alexander Arsent'evich - D.Sci, professor, head of the laboratory of system identification

220012, Minsk, Surganova st., 6

doudkin@lsi.bas-net.byБелорусский государственный университет информатики и радиоэлектроникиBelarusian state university of informatics and radioelectronicsОбъединенный институт проблем информатики НАН БеларусиUnited institute of informatics problems of NAS of Belarus201904062019012631Copyright © Перцев Д.Ю., Дудкин А.А., 20192019Перцев Д.Ю., Дудкин А.А.Pertsau D.Y., Doudkin A.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://doklady.bsuir.by/jour/article/view/1115

Представлен параметрически настраиваемый алгоритм сжатия гиперспектральных данных с применением вейвлет-разложения. Проведена оценка пропускной способности в зависимости от уровня вейвлет разложения, оценена эффективность работы представленного алгоритма.

The parametrically adjusted hyperspectral data compression algorithm based on wavelet decomposition is presented. Data stream compression throughput assessment depending on wavelet decomposition level is carried out, the overall performance of the presented algorithm is estimated.

дистанционное зондированиесжатие гиперспектральных данныхконтекстное моделированиевейвлет-разложениеremote sensinghyperspectral compressioncontext modelingwavelet-decompositionИсследование выполнено при финансовой поддержке БРФФИ (проекты Ф18ПЛШГ-008 и Ф18М-081).ReferencesAirborne Visible/Infrared Imaging Spectrometer. Официальный портал AVIRIS [Electronic resource]. URL: http://aviris.jpl.nasa.gov/ (date of access: 20.04.2018).Airborne Visible/Infrared Imaging Spectrometer. Официальный портал AVIRIS [Electronic resource]. URL: http://aviris.jpl.nasa.gov/ (date of access: 20.04.2018).Klimesh M. Low-complexity lossless compression of hyperspectral imagery via adaptive filtering. Technical Report 42-163, Jet Propulsion Laboratory, California Institute of Technology, 2005.Klimesh M. Low-complexity lossless compression of hyperspectral imagery via adaptive filtering. Technical Report 42-163, Jet Propulsion Laboratory, California Institute of Technology, 2005.Pizzolante R. Lossless compression of hyperspectral imagery // Proc. of the First International Conference on Data Compression, Communications and Processing. 2011. P. 157–162.Pizzolante R. Lossless compression of hyperspectral imagery // Proc. of the First International Conference on Data Compression, Communications and Processing. 2011. P. 157–162.Wang H., Babacan S.D., Sayood K. Lossless hyperspectral-image compression using context-based conditional average // Geoscience and Remote Sensing, IEEE Transactions on. 2007. Vol. 45, iss. 12. P. 4187–4193.Wang H., Babacan S.D., Sayood K. Lossless hyperspectral-image compression using context-based conditional average // Geoscience and Remote Sensing, IEEE Transactions on. 2007. Vol. 45, iss. 12. P. 4187–4193.Magli E., Olmo G., Quacchio E. Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC // Geoscience and Remote Sensing Letters, IEEE. 2004. Vol. 1, iss. 1. P. 21–25.Magli E., Olmo G., Quacchio E. Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC // Geoscience and Remote Sensing Letters, IEEE. 2004. Vol. 1, iss. 1. P. 21–25.Huang B., Sriraja Y. Lossless compression of hyperspectral imagery via lookup tables with predictor selection // Proc. Image and Signal Processing for Remote Sensing XII. 2006. Vol. 63–65. P. 131–139.Huang B., Sriraja Y. Lossless compression of hyperspectral imagery via lookup tables with predictor selection // Proc. Image and Signal Processing for Remote Sensing XII. 2006. Vol. 63–65. P. 131–139.Mielikainen J., Toivanen P. Lossless compression of hyperspectral images using a quantized index to lookup tables // Geoscience and Remote Sensing Letters, IEEE. 2008. Vol. 5, iss. 3. P. 474–478.Mielikainen J., Toivanen P. Lossless compression of hyperspectral images using a quantized index to lookup tables // Geoscience and Remote Sensing Letters, IEEE. 2008. Vol. 5, iss. 3. P. 474–478.Chang C.-I. Hyperspectral data processing: algorithm design and analysis. New York: John Wiley & Sons, 2013. 1164 p.Chang C.-I. Hyperspectral data processing: algorithm design and analysis. New York: John Wiley & Sons, 2013. 1164 p.Sayood Kh. Introduction to Data Compression. Morgan Kaufmann, 2017. 765 p.Sayood Kh. Introduction to Data Compression. Morgan Kaufmann, 2017. 765 p.

The authors declare that there are no conflicts of interest present.