Recently, near-field 3-D synthetic aperture radar (SAR) imaging has become a research focus in the SAR field. The target extraction is an important step in 3-D SAR image application. As far as we know, there are few studies on the target extraction for 3-D SAR images. One of the difficulties that the target extraction has to face is the influence of the environmental interference in the background. In this paper, we propose a region adaptive morphological reconstruction fuzzy C-means algorithm to overcome the influence of interference and achieve high-precision target extraction for 3-D SAR images. Firstly, 3-D anisotropic diffusion filtering achieves image smoothing with edge preserved. Secondly, a 3-D omnidirectional detection operator is constructed to obtain the gradient magnitude of the images. The target edges, which provide coarse target position information, are extracted by the hysteresis threshold method. Then, multiple sub-images are extracted from the original image based on the position information. Thereby, the influence of interference is eliminated, and the coarse target region is obtained. Finally, the adaptive morphological reconstruction fuzzy C-means algorithm is utilized to extract the target region more accurately for each sub-image. After the extraction results of all sub-images are merged into one image according to the position information, the high-precision target extraction of 3-D SAR image is completed. For the experiments, we compare the proposed algorithm and 11 algorithms through a real 3-D SAR dataset. The measurement results demonstrate that the proposed algorithm can overcome the influence of interference, and the performance of the proposed algorithm is much higher than that of the other algorithms. Moreover, the proposed algorithm has high computational efficiency.

近来，近场3D合成孔径雷达（SAR）成像已成为SAR领域的研究重点。目标提取是3D SAR图像应用中的重要步骤。据我们所知，关于3-D SAR图像目标提取的研究很少。目标提取必须面对的困难之一是背景中环境干扰的影响。本文提出了一种区域自适应形态学重构模糊C-均值算法，克服了干扰的影响，实现了3-D SAR图像的高精度目标提取。首先，3D各向异性扩散滤波可在保留边缘的情况下实现图像平滑。其次，构造3-D全向检测算子以获得图像的梯度幅度。目标边缘 通过迟滞阈值方法提取提供粗略目标位置信息的信号。然后，基于位置信息从原始图像提取多个子图像。从而，消除了干扰的影响，并且获得了粗略的目标区域。最后，利用自适应形态重构模糊C均值算法为每个子图像更准确地提取目标区域。根据位置信息将所有子图像的提取结果合并为一张图像后，完成了3-D SAR图像的高精度目标提取。对于实验，我们通过真实的3D SAR数据集比较了所提出的算法和11种算法。测量结果表明，该算法可以克服干扰的影响，该算法的性能远远高于其他算法。此外，该算法具有较高的计算效率。