Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images

This paper presents aircraft target recognition (ATR) syst em using Inverse Synthetic Aperture Radar (ISAR). The methodology used to design the complete pr ocessing chain from the acquisition step to the recognition (classification) step is based on the arti ficial intelligence approach. This process is known as Knowledge Discovery from Data (KDD) which we have ad apted to radar target recognition system. We propose a new method for target shape extraction f rom ISAR images based on the combination of a modified SUSAN Algorithm and Variational of Lev el Set. To guarantee the invariance in translation and rotation of the extracted shape, the moment invariants and Fourier descriptors are used. In the second part of this work, We have investigated the impact of the information fusion on our recognition system. Therefore, three combination strategies: probabi lity theory, majority vote and belief theory are applied at score and decision level. The classification resu lts are obtained using Support Vector Machine (SVM) classifier. In the last section, experimental results are provided and discussed.