A Novel Approach for RCS Feature Extraction Using Imaging Processing

In this paper, a novel approach for RCS feature extraction using imaging processing is proposed firstly. We take the frequency-angle RCS data sets normalized for every observation angle as intensity images with 256 gray levels, and find that different targets have different textures. This implicates that the particular textures of each image can be used to recognize the corresponding targets. Here, we chose the gray level co-occurrence matrix for texture feature extraction by using of discrete wavelet transform (DWT) to further enhance the performance of target recognition. The simulation results show that the proposed approach is of great perspective for target discrimination. By properly choice of texture features, it can provide good feature vectors for further pattern recognition