ABSTRACT

Oceanic internal waves play a crucial role in ocean activities. Currently, the approach to detecting oceanic internal waves from synthetic aperture radar (SAR) images is becoming robust. To efficiently identify the stripes of oceanic internal waves from SAR images, we propose an integrated algorithm for the detection and recognition of oceanic internal waves. First, the Gamma Map filtering method was adopted to reduce speckle noise in the SAR images. Then, histogram of orientated gradients (HOG), Grey level co-occurrence matrix (GLCM), and fractal dimension (FD) were utilized to extract the image features. Subsequently, support vector machine (SVM) was adopted to classify the SAR images and obtain images that contain oceanic internal waves. Next, the Canny edge detection method was used to detect and recognize the stripes of oceanic internal waves in the SAR images, and these stripes were screened by three parameters, namely their lengths, area ratios, and directions. Finally, the positions of the stripes of the oceanic internal waves were obtained. The experimental results verify that the proposed method can identify whether SAR images contain oceanic internal waves, and also determine the locations of their stripes in the SAR images. Meanwhile, the algorithm exhibits reasonable robustness and recognition rate. In addition, the optimal accuracy and kappa coefficient ($\kappa$) are 94.2% and 0.878, respectively.

摘要

海洋内波在海洋活动中起着至关重要的作用。目前，从合成孔径雷达 (SAR) 图像检测海洋内波的方法正在变得稳健。为了有效地从 SAR 图像中识别海洋内波的条纹，我们提出了一种用于检测和识别海洋内波的集成算法。首先，采用Gamma Map滤波方法降低SAR图像中的散斑噪声。然后，利用定向梯度直方图（HOG）、灰度共生矩阵（GLCM）和分形维数（FD）来提取图像特征。随后，采用支持向量机（SVM）对SAR图像进行分类，得到包含海洋内波的图像。下一个，利用Canny边缘检测方法对SAR图像中的海洋内波条纹进行检测和识别，这些条纹通过长度、面积比和方向三个参数进行筛选。最后，得到了大洋内波条纹的位置。实验结果验证了该方法能够识别SAR图像中是否包含海洋内波，并能确定其条纹在SAR图像中的位置。同时，该算法表现出合理的鲁棒性和识别率。此外，最佳精度和 kappa 系数（实验结果验证了该方法能够识别SAR图像中是否包含海洋内波，并能确定其条纹在SAR图像中的位置。同时，该算法表现出合理的鲁棒性和识别率。此外，最佳精度和 kappa 系数（实验结果验证了该方法能够识别SAR图像中是否包含海洋内波，并能确定其条纹在SAR图像中的位置。同时，该算法表现出合理的鲁棒性和识别率。此外，最佳精度和 kappa 系数（$\kappa$) 分别为 94.2% 和 0.878。