Fusion Fourier Descriptors from the E-M, K-Means and Fisher Algorithms for Radar Target Recognition

The target recognition from Radar images was a crucial step in our research. This paper presents a process and an adopted approach for Automatic Target recognition using Inverse Synthetic Aperture Radar (ISAR) image. Indeed, the process adopted is composed of three steps. In the first step, we achieve the edge detection using of three techniques: Fisher, Kmeans and Expectation-Maximization (E-M). Each of these techniques is combined with Watersheds (WS) algorithm to obtain the closed target shape. In order to ensure that the shape descriptors must be accurate, compact and invariant to several geometrical transformations (translation, rotation, scale, etc.), we have used Fourier Descriptor computed on each obtained shape. To achieve a classification task in the last step, several techniques can be used to perform recognition tasks. We have used the nearest-neighbor classifier to retrieve a nearest known target for each unknown target in the test dataset. Finally, in order to validate our proposed approach a database of ISAR images reconstructed from anechoic chamber simulations will be used. The simulation results using Fisher, EM and K-Means methods will be presented in the last section of this paper. KeywordsISAR image; Fisher; K-Means; E-M; Watersheds; Fourier descriptor; fusion; KNN classification