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КО, 2018, том 42, выпуск 6, страницы 1035–1045 (Mi co589)  

ОБРАБОТКА ИЗОБРАЖЕНИЙ, РАСПОЗНАВАНИЕ ОБРАЗОВ

Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performanc

M. Booriab, R. A. Paringerac, K. Choudharyda, A. V. Kupriyanovca

a Samara National Research University, 443086, Russia, Samara, Moskovskoye Shosse 34
b American Sentinel University, Colorado, USA
c IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, 443001, Samara, Russia
d The Hong Kong Polytechnic University, Hong Kong

Аннотация: The main aim of this research work is to compare k-nearest neighbor algorithm (KNN) supervised classification with migrating means clustering unsupervised classification (MMC) method on the performance of hyperspectral and multispectral data for spectral land cover classes and develop their spectral library in Samara, Russia. Accuracy assessment of the derived thematic maps was based on the analysis of the classification confusion matrix statistics computed for each classified map, using for consistency the same set of validation points. We were analyzed and compared Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space based sensors that can document detailed information on the distribution of land cover classes, sometimes species level. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). Development of spectral library for land cover classes is a key component needed to facilitate advance analytical techniques to monitor land cover changes. Different land cover classes in Samara were sampled to create a common spectral library for mapping landscape from remotely sensed data. The development of these libraries provides a physical basis for interpretation that is less subject to conditions of specific data sets, to facilitate a global approach to the application of hyperspectral imagers to mapping landscape. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes’ separation.

Ключевые слова: hyperspectral; multispectral; satellite data; land cover classification; remote sensing; supervised and unsupervised classification; spectral library.

Финансовая поддержка Номер гранта
Российский фонд фундаментальных исследований 16-41-630761 р_а
16-29-11698 офи-м
17-01-00972 а
Министерство образования и науки Российской Федерации
This work was partially supported by the Ministry of education and science of the Russian Federation; by the Russian Foundation for Basic Research grants (# 16-41-630761; # 16-29-11698, # 17-01-00972).


DOI: https://doi.org/10.18287/2412-6179-2018-42-6-1035-1045

Полный текст: PDF файл (3094 kB)
Полный текст: http://www.computeroptics.smr.ru/.../420612.html
Список литературы: PDF файл   HTML файл

Тип публикации: Статья
Поступила в редакцию: 13.06.2018
Принята в печать:20.11.2018
Язык публикации: английский

Образец цитирования: M. Boori, R. A. Paringer, K. Choudhary, A. V. Kupriyanov, “Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performanc”, КО, 42:6 (2018), 1035–1045

Цитирование в формате AMSBIB
\RBibitem{BooParCho18}
\by M.~Boori, R.~A.~Paringer, K.~Choudhary, A.~V.~Kupriyanov
\paper Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performanc
\jour КО
\yr 2018
\vol 42
\issue 6
\pages 1035--1045
\mathnet{http://mi.mathnet.ru/co589}
\crossref{https://doi.org/10.18287/2412-6179-2018-42-6-1035-1045}


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