Method for fine pattern recognition of space targets using the entropy weight fuzzy-rough nearest neighbor algorithm

Abstract

In space target recognition using spectral analysis technology, there is the problem that the composition or chemical properties of surface materials of the space target are similar. This problem leads to the high similarity of spectral curves and low accuracy of space target recognition. Similar object recognition is important in the study of actual space target observation. In this paper, an entropy weight fuzzy-rough nearest neighbor (EFRNN) algorithm is proposed to enhance the recognition accuracy of similar space targets, which is an improvement of the fuzzy-rough nearest neighbor algorithm. By introducing the feature weight determined using information entropy, the features of all the training samples are considered and quantified. Moreover, the proposed algorithm combined with fuzzy-rough set theory can overcome the fuzzy uncertainty caused by overlapping classes and the rough uncertainty caused by insufficient features, to a certain extent. The simulation results show that the proposed algorithm achieves very promising performance compared with existing algorithms. The EFRNN classifier yields an overall classification accuracy of 95.83%. The proposed algorithm is simple and efficient for similar space target recognition. Furthermore, the EFRNN algorithm does not require preset parameters and complex preprocessing.