DBPapers
DOI: 10.5593/sgem2017H/43/S19.075

USING HYPERSPECTRAL AND MULTI-SPECTRAL REMOTE SENSING DATA TO BUILD SPECTRAL LIBRARY FOR LAND COVER CLASSIFICATION IN SAMARA, RUSSIA

M. S. Boori, R. Paringer, K. Choudhary, A. Kupriyanov
Thursday 23 November 2017 by Libadmin2017

References: 17th International Multidisciplinary Scientific GeoConference SGEM 2017, www.sgemviennagreen.org, SGEM2017 Vienna GREEN Conference Proceedings, ISBN 978-619-7408-28-7 / ISSN 1314-2704, 27 - 29 November, 2017, Vol. 17, Issue 43, 593-600 pp; DOI: 10.5593/sgem2017H/43/S19.075

ABSTRACT

The main aim of this research work is to compare the performance of hyperspectral and multispectral data for spectral land cover classes and develop their spectral library in Samara, Russia. 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. 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. The results show that hyperspectral satellite imagery is suitable for land cover classification till species level. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image.

Keywords: Hyperspectral; multispectral; satellite data; land cover classification; remote sensing; spectral library.