Feature extraction from high-dimensional synthetic aperture radar (SAR) images is one of the crucial steps for SAR automatic target recognition (ATR). In this paper, we propose a new approach to SAR images feature extraction named Two-dimensional Principal Component Analysis-based Two-dimensional Maximum Interclass Distance Embedding (2DPCA-based 2DMIDE) which is based on manifold learning theory. The SAR image is projected into the feature space by horizontal 2DPCA and vertical 2DMIDE sequentially through this method. 2DPCA is efficient for image representation and preserves the global spatial structure of the original image, while 2DMIDE seeks to preserve the local spatial structure and the intrinsic geometry of the original image. Therefore, this feature extraction algorithm which fuses 2DPCA and 2DMIDE techniques can not only represent the original image in lower dimensions, but also excavate more powerful recognition information effectively. The experiment based on MSTAR database shows that the proposed method has a better recognition performance.
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