Heterogeneous forest classification by creating mixed vegetation classes using EO-1 Hyperion

Márton Deák, Tamás Telbisz, Mátyás Árvai, László Mari, Ferenc Horváth, Balázs Kohán, Orsolya Szabó, József Kovács

Research output: Contribution to journalArticle

6 Citations (Scopus)


Hyperspectral satellite data is an efficient tool in vegetation mapping; however, previous studies indicate that classifying heterogeneous forests might be difficult. In this study, we propose a mapping method for a heterogeneous forest using the data of the Earth Observing-1 (EO-1) Hyperion supplemented by field survey. We introduced a band reduction method to raise classification accuracy of the Support Vector Machine classification algorithm and compared the results to the one reduced by principal component analysis (PCA), stepwise discriminant analysis (SDA), and the original data set. We also used a modified version of the Vegetation–Impervious–Soil model to create mixed vegetation classes consisting of the commonly mixing species in the area and classified them using Decision Tree classification method. We managed to achieve 84.28% approximately using our band reduction method which is 2.36% increase compared to PCA (81.92%), 1.43% compared to the SDA (82.85%), and 7.61% compared to the original data set (76.67%). Introducing the mixed vegetation classes raised the overall accuracy even higher (85.79%).

Original languageEnglish
Pages (from-to)5215-5231
Number of pages17
JournalInternational Journal of Remote Sensing
Issue number18
Publication statusPublished - Sep 17 2017

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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