Region adaptive morphological reconstruction fuzzy C-means for near-field 3-D SAR image target extraction

Abstract

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.