Hiperspektral görüntülerde Relief-F algoritması ile band seçimi.

Hyperspectral images contain detailed information for classification. However, these data negatively affect the classification results due to their high size, large data volume and strong correlation between adjacent bands. Classification efficiency and accuracy of hyperspectral images can be improved with an appropriate feature selection method. In this study, the Relief-F feature selection algorithm was preferred due to its features such as being independent of the classification model, not taking into account the assumption of multicollinearity, and being able to process noise values. Salinas-A, Indian Pines and Pavia University datasets were used as experimental data to examine the application effect of the Relief-F algorithm. After the applications, the Support Vector Machine classifier showed higher performance in the Salinas-A and Indian Pines datasets after band selection; It has been observed that the classification accuracy of the Random Forest method is largely preserved. The research results show that the Relief-F algorithm determines the most necessary features in hyperspectral images and the number of bands can be reduced by 60% - 70% with a good classification accuracy. [ABSTRACT FROM AUTHOR]