Classification on Hyperspectral Images Using Enhanced Fisher Discriminant Criterion

Hyperspectral image processing has become an important research topic day by day. Due to the improvement in camera technology, the easiness in data acquisition has a result of born of new application areas. One of the research topics in hyperspectral image processing is dimension reduction. Dimension reduction is a widely used method in pattern recognition when dealing with high dimensional data. In this paper, a method based on enhanced Fisher discriminant criterion (EFDC), which is proposed by Gao et al. (Q. Gao, J.Liu, H. Zhang, J. Hou and X. Yang, "Enhanced fisher discriminant criterion for image recognition", Pattern Recognition, vol. 45, pp. 3717-3724, 2012) is proposed for classification on hyperspectral images. In the proposed method, EFDC is used for dimension reduction. In the proposed method, a classification process on hyperspectral imagesis done by using dimension reduced data. According to the earliest experimental studies, the promising results are obtained for classification on hyperspectral images.