Greedy algorithms of feature selection for multiclass image classification
To improve the performance of remote sensing images multiclass classification we propose two greedy algorithms of feature selection. The discriminant analysis criterion and regression coefficients are used as the measure of feature subset effectiveness in the first and second methods respectively. The main benefit of the built algorithms is that they estimate not the individual criterion for each feature, but the general effectiveness of the feature subset. As there is a big limitation on the number of real remote sensing images, available for the analysis, we apply the Markov random model to enlarge the image dataset. As the pattern for image modelling, a random image belonging to one of the 7 classes from the UC Merced Land-Use dataset has been used. Features have been extracted with help of MaZda software. As the result, the largest fraction of correctly classified images accounts for 95%. Dimension of the initial feature space consisting of 218 features has been reduced to 15 features, using the greedy strategy of removing a feature, based on the linear regression model.