A new per-field classification method using mixture discriminant analysis

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.

Keywords:

• average Bhattacharyya distance
• Gaussian mixture discriminant analysis
• per-field classification
• per-pixel classification
• supervised classification

Acknowledgements

This work was supported by the TUBITAK BAYG (No.2211) and Çukurova University Scientific Research Project Unit (No. FEF 2008D16 LTP). The authors thank the editor and especially two of the anonymous referees for carefully reading the manuscript and making some valuable comments which had greatly improved the earlier draft of the manuscript.