基于粒子群算法的SAR图像海洋内波检测

C. Divya, S. Vasavi, A. Sarma
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引用次数: 1

摘要

内波是发生在深海的波。许多研究人员发现了探测内波的不同方法。众所周知,机器学习是解决复杂问题发展最快的技术。因此,在本文中,我们利用这个优势来检测内波,因为这也被认为是一个复杂的问题。在此基础上,提出了一种利用神经网络进行内波检测的新方法。本文提出了一种基于粒子群算法的内波自动检测系统。该系统可以与表面小波和光滑进行区分。介绍了SAR图像内波参数的提取方法。初步建立了基于优化算法的海洋内波探测模型。在该方法中,将构建特征表示模型和预测模型两个模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ocean Internal Wave Detection from SAR Images using Particle Swarm Optimization
The internal waves are the waves that occur in the deep ocean. Many researchers found different methods for the detection of internal waves. As we all know that machine learning is the fastest growing technology to solve complicated problems. So, here in this paper we took this advantage for the detection of internal waves because this is also considered as a complicated problem. Based on this a novel method is proposed for internal wave detection using neural networks which is a method that acts as a human brain. A novel system using Particle swarm optimization is proposed in this paper to automatically detect internal waves. Such system can differentiate with surface wavelets and slicks. This paper describes extraction of internal wave parameters from SAR images. Initially ocean internal wave detection is modeled based on optimization algorithms. In the proposed method, two models such as feature representation model and prediction model will be build.
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