Feature Extraction and Fusion for Automatic Target Recognition Based ISAR Images

Abstract

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