Fast fusion method of marine environment vector data based on BP neural network

Wenyan Wang, Hai-xiao Gong
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引用次数: 1

Abstract

The fast fusion method of marine environment vector data currently studied requires high light wave resolution in the panchromatic band and multi-spectral band, and the fused data is relatively fuzzy. In view of the above problems, a new marine environment vector based on BP neural network is studied. Fast data fusion method, accurate data acquisition operation, the data is arranged according to the filter length, system collection and filtering operations are performed according to the arrangement standard, and effective data is obtained. Even the low-resolution data can be well after the filtering operation. Fusion, based on the collected data, effectively analyze the fusion rules, combine the data wavelength center point to perform point data collection operations, combine the collected data, improve the system combination performance, and record the data that meets the combination standard as a fusion The standards are stored in the system space. Finally, the fusion criteria are used to perform data fusion operations to match the data fusion similarity, and the data that meets the system similarity standards are retained, and irrelevant data parameters are filtered to achieve accurate and automated data fusion. The experimental results show that the fast fusion method of marine environment vector data based on BP neural network can analyze the light wave resolution of panchromatic band and multispectral band well, and can realize high-definition data fusion.
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基于BP神经网络的海洋环境矢量数据快速融合方法
目前研究的海洋环境矢量数据快速融合方法对全色波段和多光谱波段的光波分辨率要求较高,融合后的数据相对模糊。针对上述问题,研究了一种新的基于BP神经网络的海洋环境向量。快速的数据融合方法,准确的数据采集操作,将数据按照过滤长度进行排列,系统按照排列标准进行采集和过滤操作,获得有效数据。即使是低分辨率的数据,经过滤波处理后也能很好地显示。融合,基于采集到的数据,有效分析融合规则,组合数据波长中心点进行点数据采集操作,将采集到的数据进行组合,提升系统组合性能,将符合组合标准的数据作为融合记录,将标准存储在系统空间中。最后,利用融合准则进行数据融合操作,匹配数据融合相似度,保留符合系统相似度标准的数据,过滤不相关的数据参数,实现准确、自动化的数据融合。实验结果表明,基于BP神经网络的海洋环境矢量数据快速融合方法能够很好地分析全色波段和多光谱波段的光波分辨率,能够实现高分辨率的数据融合。
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