Sar Automatic Target Recognition Based On Manifold Method And Nuclear Research

Synthetic Aperture Radar (SAR) is high-resolution imaging radar, with its all-time, all-time and strong penetration ability. It provides a reliable data source for target recognition. SAR automatic target recognition (ATR) is an effective SAR image interpretation tool without participation of humans. SAR ATR is widely used in civilian fields, and become a hot challenging subject.This dissertation covers two parts of SAR ATR system:SAR image pre-processing and feature extraction. Main content of this dissertation are summarized as follows:Firstly, according to the problem of SAR images, frost filter is utilized to suppress speckle, gray enhancement is used to enhance information in SAR images, two preprocessing CFAR segmentation based on Gaussian distribution is utilized to remove background clutters. Though these methods, the detail information of SAR image is kept, and data dimension can be reduced effectively.Secondly, feature extraction based on global linear structure can not extract feature of high dimensional data, Maximum Interclass Distance Embedding (MIDE) is proposed for feature extraction. This algorithm is based on manifold learning. It can solve nonlinear feature extraction of high dimensional data and improve recognition performance effectively.Thirdly, it may lose structure information of image to use MIDE, Two dimensional Maximum Interclass Distance Embedding (2DMIDE) is proposed. This algorithm can extract2D SAR image directly, it can both keep structure information and improve recognition performance. According to the problem that there is high dimension feature through2DMIDE,2D Principal Component Analysis (2DPCA)-based2DMIDE is proposed. This algorithm can compress SAR images on both horizontal and vertical, which reduce feature dimension effectively. Experiment based on MSTAR shows that2DPCA-based2DMIDE can improve recognition performance notability.Forthly, MIDE can not extract data feature without class information, Maximum Variance Unfolding Embedding (MVUE), an unsupervised feature extraction algorithm, is proposed. This algorithm combines manifold learning with kernel trick, it can improve recognition performance.Finally, experiments based on MSTAR show that SAR ATR pre-processing and several algothms proposed in this dissertation can improve SAR ATR obviously.