基于二维能量检测的舰船SAR图像阈值分割

Ship SAR image threshold segmentation based on two-dimensional energy detection

鉴于传统阈值分割算法过于依赖背景杂波分布模型,以及在抗噪性、鲁棒性等方面的不足,文章通过改进传统能量检测算法的局部信杂比模型,提出了基于λ检测的算法,解决了阈值不能自适应选择的问题。并针对图像存在大量相干斑及拖影时,算法处理能力不足的问题,考虑邻域像素均值μ将其拓展到二维,提出了基于二维能量检测的阈值分割法。最后,通过引入域内一致性、域间差异性和形状复杂度3个指标,与目前流行的最大熵阈值法以及改进的二维最大类间差法做对比实验,结果证明了本文算法简单有效。

In view of the fact that the traditional threshold segmentation algorithm relies on the distribution model of the background clutter severely and its poor performance in noise resistance and robustness, such an algorithm as λ-detection is proposed, which is based on the improvement of the local signal-to-noise ratio model in the traditional energy detection, and the problem that the threshold cannot be adaptively selected is solved. Aiming at the problem that the algorithm has insufficient processing power when there are a large number of speckles and smear in the synthetic aperture radar (SAR) image, the neighborhood pixel mean is considered to extend it to two-dimensional, then the threshold segmentation method based on the two-dimensional energy detection comes out. Finally, by introducing three indicators of uniformity of intra region, dissimilarity of inter region and shape complexity, compared with the popular maximum entropy threshold segmentation and improved 2D maximum inter-class difference method, the proposed algorithm has been proved simple and effective.