...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ISPRS International Journal of Geo-Information >Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images
【24h】

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

机译：用于从SAR图像提取道路的多尺度检测器的贝叶斯融合

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper introduces an innovative road network extraction algorithm using synthetic aperture radar (SAR) imagery for improving the accuracy of road extraction. The state-of-the-art approaches, such as fraction extraction and road network optimization, failed to obtain continuous road segments in separate successions, since the optimization could not change the parts ignored by the fraction extraction. In this paper, the proposed algorithm integrates the fraction extraction and optimization procedure simultaneously to extract the road network: (1) the Bayesian framework is utilized to transfer the road network extraction to joint reasoning of the likelihood of fraction extraction and the priority of network optimization; (2) the multi-scale linear feature detector (MLFD) and the network optimization beamlet are introduced; (3) the conditional random field (CRF) is used to reason jointly. The result is the global optimum since the fraction extraction and network optimization are exploited at the same time. The proposed algorithm solves the problem that the fractions are bound to reduce in the process of network optimization and has demonstrated effectiveness in real SAR images applications.
机译：本文介绍了一种利用合成孔径雷达（SAR）图像进行创新的路网提取算法，以提高路段提取的准确性。最新的方法（例如分数提取和道路网络优化）无法在单独的连续序列中获得连续的路段，因为优化无法更改分数提取所忽略的部分。本文提出的算法同时集成了分数提取和优化过程，以提取道路网络：（1）利用贝叶斯框架将道路网络提取转移到分数提取的可能性与网络优化优先级的联合推理中。 ; （2）介绍了多尺度线性特征检测器（MLFD）和网络优化子束。 （3）条件随机场（CRF）用于联合推理。结果是全局最优的，因为分数提取和网络优化是同时进行的。该算法解决了网络优化过程中分数必然减少的问题，并在实际SAR图像应用中证明了其有效性。

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