A new per-field classification method using mixture discriminant analysis

• Published in: Journal of applied statistics Vol. 39; no. 10; pp. 2129 - 2140
• Main Authors: Çalış, Nazif, Erol, Hamza
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01-10-2012; Taylor & Francis Ltd
• Subjects: Applied statistics; average Bhattacharyya distance; Classification; Covariance matrix; Decision theory; Discriminant analysis; Discrimination; Gaussian; Gaussian mixture discriminant analysis; Mathematical models; Normal distribution; per-field classification; per-pixel classification; Remote sensing; Studies; supervised classification
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2012.702263

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.