It is challenging to estimate ocean surface currents from along-track interferometric (ATI) data due to the complexity of the ocean environment. In order to complete the process from modeling the ocean surface signals to extracting the ocean surface currents, this research integrates dynamic sea surface modeling imagery with an iterative technique to inverse the currents. To complete the simulation analysis of time-varying ocean surface imaging, ocean surface backscatter coefficients and ocean surface echoes are constructed using the wave spectrum model as the basis. The ATI data from the simulation modeling are inverted using the iterative inversion algorithm of the Synthetic Aperture Radar (SAR) ocean surface currents field, and the accuracy of the inverse ocean surface currents field obtained reaches 0.1 m/s, proving the viability of the whole system.
Although the data on a particular complicated sea conditions are available, it is difficult to collect the data on various complicated sea conditions simultaneously. When the sea conditions change, the test results of the detection network based on the data of simple sea conditions may be unacceptable. It is proposed that the VGG16, pix2pixHD, and cycleGAN methods should be applied to establish related datasets of different complicated sea conditions. After the unified image quality assessment indicators are determined, it is found that the images generated by the VGG16 network are most in line with the subsequent target detection standards. The real images of complicated sea conditions have the smallest root-mean-square error, the largest peak signal-to-noise ratio, and the best regression coefficient (R2). Meanwhile, the improved Faster R-CNN is introduced for target detection of small sample datasets. First, the ResNet50_FPN module, as the backbone feature extractor, is used to improve the detection performance of small-sized objects. Moreover, because there are many small target objects to be detected, the ROI Align module is preferred, for it is more accurate. Finally, the Softer-NMS algorithm is selected to significantly improve the positioning accuracy through confidence estimation. Compared with some previous Faster R-CNN methods, the accuracy and missed detection rate of the proposed method outperform other networks, with a mean average precision of 85.11%.
We propose an innovative method for ship detection in the real marine environment. Different from other high-resolution optical images and synthetic aperture radar images, ship detection of coast defense radar is quite challenging due to the complex background, sea state, and low resolution. To this end, we build a real dataset and propose an innovative detection method based on you only look once (YOLO) V4. Specifically, first, the lightweight architecture MobileNetV3 is introduced as the backbone feature extractor to accelerate the detection speed by compressing the parameters. Second, for better detection of small-size ships, the squeeze-and-excitation module is used to apply the attention mechanism to the channel. Meanwhile, the scaled exponential linear unit non-liner activation function replaces the rectified linear unit activation function of the MobileNetV3 shallow layer, which optimizes the convergence effect of the model. Third, an adaptive anchor-selection algorithm for the detection of ships with various shapes is designed. Compared with other well-established models based on convolutional neural network (CNN), including single shot multi-box detector, Faster region based-CNN, and you only look once version 4 baseline for detecting ships, our improved method yields impressive results in our dataset. After extensive testing, the mean average precision of the proposed method can reach 97.43%, with the detection time per frame reaching 38 ms.
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