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A new per-field classification method using mixture discriminant analysis

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  • Nazif Çalış
  • Hamza Erol

Abstract

In this study, a new per-field classification method is proposed for supervised classification of remotely sensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis (MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructed for control and test fields can have fixed or different number of components and each component can have different or common covariance matrix structure. The discrimination function and the decision rule of this method are established according to the average Bhattacharyya distance and the minimum values of the average Bhattacharyya distances, respectively. The proposed per-field classification method is analyzed for different structures of a covariance matrix with fixed and different number of components. Also, we classify the remotely sensed multispectral image data using the per-pixel classification method based on Gaussian MDA.

Suggested Citation

  • Nazif Çalış & Hamza Erol, 2012. "A new per-field classification method using mixture discriminant analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(10), pages 2129-2140, June.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2129-2140
    DOI: 10.1080/02664763.2012.702263
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    1. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard & Langrognet, Florent, 2006. "Model-based cluster and discriminant analysis with the MIXMOD software," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 587-600, November.
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