...
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Synthetic Aperture Radar Ship Detection Using Haar-Like Features
【24h】

Synthetic Aperture Radar Ship Detection Using Haar-Like Features

机译:使用类似Haar的特征的合成孔径雷达舰船检测

获取原文
获取原文并翻译 | 示例
           

摘要

The detection of ships at sea is a complex task made more so by adverse weather conditions, lack of night visibility, and large areas of concern. Synthetic aperture radar (SAR) imagery with large swaths can provide the needed coverage at a reduced resolution. The development of ship detection methods that can effectively detect ships despite the reduced image resolution is an important area of research. A novel ship detection method is introduced that makes use of a standard constant false alarm rate (FAR) prescreening step followed by a cascade classifier ship discriminator. Ships are identified using Haar-like features using adaptive boosting training on the classifier with an accuracy of 89.38% and FAR of 1.47 × 10-8 across a large swath Sentinel-1 and RADARSAT-2 newly created SAR data set.
机译:恶劣的天气条件,缺乏夜间能见度以及大面积的关注区域使海上船舶的检测成为一项复杂的任务。大范围的合成孔径雷达(SAR)图像可以降低分辨率提供所需的覆盖范围。尽管图像分辨率降低,但仍可有效检测船舶的船舶检测方法的开发是重要的研究领域。介绍了一种新颖的船舶检测方法,该方法利用标准的恒定误报率(FAR)预筛选步骤,然后进行级联分类器船舶识别器。在类似Sarnel-1和RADARSAT-2的新创建的SAR数据集上,使用类似于Haar的特征,通过在分类器上进行自适应增强训练来识别船舶,其准确度为89.38%,FAR为1.47×10-8。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号