Classification of radar images with different methods of image preprocessing

Abstract

This work is aimed at comparing the classification algorithms and methods of machine learning with various methods of preliminary processing of radar images. Preprocessing step includes speckle noise filtering and object orientation normalization on the image. In comparison, the following classification algorithms were considered: Decision Tree, Support Vector Machine, K Nearest-Neighbor method, Random Forest, AdaBoost where decision tree was used as a weak classifier, Convolutional Neural Network and Residual Neural Network. The principal component analysis was applied to reduce the dimension. The study was carried out on the objects from the base of radar images MSTAR.