海洋油膜检测方法基于不断增长的层次神经气体网络和多尺度值细分
Baozhu Jia1, Zekun Guo2, Jin Xu1
1Shenzhen Institute of Guangdong Ocean University, Shenzhen, 518116, China; Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, 524091, China; Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province, Zhanjiang, 524088, China; Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang, 524088, China.
本研究介绍了一种先进的油膜检测方法,使用增长的层次神经气体网络 (GHNG) 和海洋雷达图像的多尺度值. 该技术可以在复杂的海上条件下加强海上石油泄漏监测.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 海上石油活动增加了漏油风险,威胁到海洋生态系统.
- 可靠的油膜监测技术对于环境保护至关重要.
研究的目的:
- 为海洋雷达图像开发先进的油膜检测方法.
- 为了应对弱油膜特征和环境噪声的挑战.
主要方法:
- 利用成长的等级神经气体网络 (GHNG) 进行无监督学习,动态拓学习和等级集群.
- 应用多尺度自适应值细分,用于精确的油膜目标提取.
- 集成的噪声过和坐标转换用于最终细分.
主要成果:
- 高温天然气网络有效地区分了油膜区域和背景干扰.
- 多尺度值准确提取的油膜目标.
- 该方法在复杂的海洋条件下证明了其有效性.
结论:
- 提出的方法为自动和精确的海洋油膜监测提供了有效的解决方案.
- 这项技术对于减轻石油泄漏对环境造成的破坏至关重要.
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