• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Image Processing >Oil Tank Detection Using Co-Spatial Residual and Local Gradation Statistic in SAR Images
【24h】

Oil Tank Detection Using Co-Spatial Residual and Local Gradation Statistic in SAR Images

机译：SAR图像中基于空间余量和局部灰度统计的油箱检测

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

摘要

The advance in synthetic aperture radar (SAR) technology reduces the difficulty of data acquisition, along with greatly increased computational complexity. This paper cares about this and aims to accomplish oil tank detection in SAR images from the perspective of computer vision. The whole model includes three main steps. The first is a saliency-driven clustering to accomplish the single image saliency analysis according to the intensity specificity and texture distribution. The second step introduces a common visual saliency analysis based on the co-spatial residual and local gradation statistic to extract the common visual salient parts within the input series. The final step considers the distribution of adjacent highlight points obtained from the saliency analysis to accomplish the location of tanks. Three competing methods are established in the experimental part. The evaluation in pixel level and geometric segmentation accuracy both verify the superiority of the proposed model in target detection and interferences exclusion.
机译：合成孔径雷达（SAR）技术的进步降低了数据采集的难度，并且大大增加了计算复杂度。本文对此很在意，旨在从计算机视觉的角度完成SAR图像中的油箱检测。整个模型包括三个主要步骤。第一个是显着性驱动的聚类，根据强度特异性和纹理分布来完成单个图像的显着性分析。第二步介绍了基于共同空间残差和局部灰度统计的通用视觉显着性分析，以提取输入序列中的通用视觉显着部分。最后一步考虑了从显着性分析获得的相邻高光点的分布，以完成水箱的位置。实验部分建立了三种竞争方法。像素水平和几何分割精度的评估都验证了该模型在目标检测和干扰排除方面的优越性。

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