Comparison of classification accuracy of co-located hyperspectral & multispectral images for agricultural purposes

Selin Bostan, Mehmet Akif Ortak, Caglayan Tuna, Alper Akoguz, Elif Sertel, Burak Berk Ustundag

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

The aim of this study is to compare the classification accuracy of multispectral Landsat 8 and hyperspectral EO-1 Hyperion satellite image data of the same region for agricultural purposes. Classification of hyperspectral remote sensing data is more challenging than multispectral data due to high amount of spectral information recorded in several image bands; therefore, Principal Component Analysis (PCA) was applied to these images for dimension reduction. Support Vector Machines (SVM) approach was used for classification of two different data considering the successive results obtained in latest research by applying SVM. Six different land cover classes, namely maize, cotton, urban, water, barren rock and other crop types were determined in this study and training areas were selected for each class during the training selection stage. 200 ground control points were selected within 135 km2 study area to conduct classification accuracy assessment. The overall classification accuracy of Hyperion image was found around 80%, whereas overall classification accuracy of Landsat image was found approximately 70%.

Original languageEnglish
Title of host publication2016 5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509023509
DOIs
Publication statusPublished - 26 Sept 2016
Event5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016 - Tianjin, China
Duration: 18 Jul 201620 Jul 2016

Publication series

Name2016 5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016

Conference

Conference5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016
Country/TerritoryChina
CityTianjin
Period18/07/1620/07/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Dimension reduction
  • EO-1 Hyperion
  • hyperspectral classification
  • Landsat 8
  • PCA
  • SVM

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