白冠自动识别算法的比较、改进及应用研究

Study on Comparison, Improvement and Application of Whitecap Automatic Identification Algorithm

摘要:

海洋白冠是一种典型的海表现象,对白冠覆盖率(WC)的研究具有重要的科学意义和实用价值。基于数字图像处理的白冠自动识别技术具有快速、高效、低成本和大批量的特点,对比分析了自动提取白冠算法、自适应阈值分割算法和迭代类间方差算法等自动识别算法对海面图像的处理结果,针对光照不均的海面图像提出了应用顶帽变换和图像增强的光照校正改进算法,来消除阳光反射带来的负面影响和运算不稳定。对船拍视频应用该改进算法,在光照不均时,增强了原三种算法的鲁棒性,有效提高了WC的计算正确率,有利于自动化处理视频序列图像。

Abstract:

Ocean whitecap is a typical sea surface phenomenon which is extremely significant and valuable to research on wave breaking. The features of whitecap automatic identification based on digital image processing are fast, efficient, low cost and large quantity. There are three kinds of whitecap automatic identification algorithm, such as AWE (automated whitecap extraction), ATS (adaptive thresholding segmentation) and IBCV (iterative between class variance). The result of sea surface image processed through these automatic identification algorithms are compared and analyzed in this paper. Aimed on uneven illumination of sea surface image and unstable operation results, it is proposed a kind of illumination correction algorithm using top-hat transform to eliminate negative impact by sunshine reflection and make operation stable using image enhancement technology. Experiments based on the shipboard video identify this modified method enhances robustness of the original algorithms and improves the computational efficient of WC so that it advantages for automated processing sequence images.