Hyperspectral satellite data is an efficient tool in vegetationmapping; 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 com- pared 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 com- monly 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%).
Deák M ... Horváth F ... Kovács J: Heterogeneous forest classification by ... (2017)
Deák, M., T. Telbisz, M. Árvai, L. Mari, F. Horváth, B. Kohán, O. Szabó & J. Kovács
Heterogeneous forest classification by creating mixed vegetation classes using EO-1 Hyperion
International Journal of Remote Sensing, 38(18): 5215–5231.