Ensemble classifier using GRG algorithm for land cover classification

Bolanle T. Abe; J. A. Jordaan; Tshilidzi Marwala

doi:10.1117/12.2050094

24 December 2013 Ensemble classifier using GRG algorithm for land cover classification

Bolanle T. Abe, J. A. Jordaan, Tshilidzi Marwala

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90670O (2013) https://doi.org/10.1117/12.2050094
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

Image processing is of great value because it enables satellite images to be translated into useful information. The preprocessing of remotely sensed images before features extraction is important to remove noise and improve the ability to interpret image data more accurately. All images should appear as if they were acquired from the same sensor at the end of image preprocessing. A major challenge associated with hyperspectral imagery in remote sensing analysis is the mixed pixels which are due to huge dimension nature of the data. This study makes a positive contribution to the problem of land cover classification by exploring Generalized Reduced Gradient (GRG) algorithm on hyperspectral datasets by using Washington DC mall and Indiana pines test site of Northwestern Indiana, USA as study sites. The algorithm was used to estimate the fractional abundance in the datasets for land cover classification. Ensemble classifiers such as random forest, bagging and support vector machines were implemented in Waikato Environment for knowledge Analysis (WEKA) to carry out the classification procedures. Experimental results show that random forest ensemble outperformed the other ensemble methods. The comparison of the classifiers is crucial for a decision maker to consider compromises in accuracy technique against complexity technique.

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Bolanle T. Abe, J. A. Jordaan, and Tshilidzi Marwala "Ensemble classifier using GRG algorithm for land cover classification", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670O (24 December 2013); https://doi.org/10.1117/12.2050094

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KEYWORDS

Image processing

Hyperspectral imaging

Remote sensing

Sensors

Image sensors

Earth observing sensors

Error analysis

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