Feature selection methods for remote sensing images classification
Different methods of feature selection are used to improve the performance of remote sensing images classification. In this work two methods of feature selection are examined. The first one is based on the discriminant analysis, and the second one rests on building the regression model. Histogram and textural features are considered as characteristics of an image. The experiments on the remote sensing dataset UC Merced Land Use show the effectiveness of these methods. As the result, the largest fraction of correctly classified images accounts for the 95%. Dimension of the initial feature space consisting of 18 features has been reduced to 3 features.