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SAR Image Nearshore Ship Target Detection in Complex Environment

机译：SAR图像临近复杂环境中的船舶目标检测

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摘要

With the development of depth learning and synthetic aperture radar (Synthetic Aperture Radar, SAR) technology, SAR image target detection based on convolution neural network (convolutional neural network, CNN) has achieved certain results. However, there are still problems in SAR detection of near-shore ship targets in complex environments. For improving the detection performance of the algorithm, the detection rate of SAR image near shore ship targets in complex environment is improved. This paper proposes an algorithm for SAR image ship target detection in complex environment. The algorithm first uses convolution neural network for coastal segmentation, and SAR image ship target detection through the results of coastal segmentation. The experimental results show that the algorithm has efficient detection ability for SAR image near-shore ship target detection in complex environment.
机译：随着深度学习和合成孔径雷达（合成孔径雷达，SAR）技术的发展，基于卷积神经网络（卷积神经网络，CNN）的SAR图像目标检测已经实现了某些结果。然而，在复杂环境中的近岸船舶目标的SAR检测中仍存在问题。为了提高算法的检测性能，改善了复杂环境中的岸船目标附近的SAR图像的检测速率。本文提出了一种在复杂环境中的SAR图像船舶目标检测算法。该算法首先使用沿海分割的卷积神经网络，并通过沿海分割结果进行SAR图像船舶目标检测。实验结果表明，该算法在复杂环境中具有高效的SAR图像近岸船舶目标检测的检测能力。

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