基于Sentinel-1数据融合多极化波段的海洋表面目标特征提取

T. T. Sreeranju, S. Lekshmi, P. Naresh
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引用次数: 0

摘要

新一代高分辨率SAR图像的引入有助于有效监测陆地和海洋表面。获取的SAR图像经常受到散斑噪声的影响,这给海洋区域的高质量解译带来了困难。在其他图像处理任务之前,对这些图像应用斑点减少技术来降低噪声。本文分别在Sentinel 1A数据的VV和VH极化波段采用了三种不同的散斑消减方法。利用平稳小波变换对滤波后的图像进行不同组合融合。分析了融合图像的性能指标,并从最佳融合图像中提取船舶参数和波浪特征等特征。关键词:SAR,散斑噪声,滤波,融合,雷达截面,海浪谱基于Sentinel-1数据融合多极化波段的海洋表面目标特征提取遥感与地理信息系统学报。2018;9 (2): 10-16p。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ocean Surface Target Feature Extraction from Fused Multi-Polarized Bands of Sentinel-1 Data
The introduction of new generation high-resolution SAR imagery helps in the effective monitoring of land and ocean surfaces. The SAR imagery acquired are often degraded by speckle noise which makes it difficult for quality interpretation mainly in ocean domain. Speckle reduction techniques are applied to these images to reduce the noise prior to other image processing tasks. In this paper, three different speckle reduction methods are applied both in VV and VH polarized bands of Sentinel 1A data. The filtered images are fused with different combinations using stationary wavelet transform. The performance measures of fused images are analyzed and features such as ship parameters and wave characteristics are extracted from the best-fused image. Keywords: SAR, speckle noise, filtering, fusion, radar cross section, ocean wave spectra Cite this Article Sreeranju TT, Lekshmi S, Praveen Naresh. Ocean Surface Target Feature Extraction from Fused Multi-Polarized Bands of Sentinel-1 Data. Journal of Remote Sensing & GIS. 2018; 9(2): 10–16p.
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