Stripe detection and recognition of oceanic internal waves from synthetic aperture radar based on support vector machine and feature fusion

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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 () are 94.2% and 0.878, respectively.