...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >SAR Image Despeckling Using Edge Detection and Feature Clustering in Bandelet Domain
【24h】

SAR Image Despeckling Using Edge Detection and Feature Clustering in Bandelet Domain

机译：Bandelet域中使用边缘检测和特征聚类的SAR图像去斑

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To effectively preserve the edges of a synthetic aperture radar (SAR) image when despeckling, an algorithm with edge detection and fuzzy clustering in the translation-invariant second-generation bandelet transform (TIBT) domain is proposed in this letter. A Canny operator is first utilized to detect and remove edges from the SAR image. Then, TIBT and fuzzy C-mean clustering are employed to decompose and despeckle the edge-removed image, respectively. Finally, the removed edges are added to the reconstructed image. The algorithm suggests each coefficient in high-frequency subbands as the clustering feature, proposes a calculation method of the best clustering number, and defines the signal and noise in the clustering results. Experimental results show that the visual quality and evaluation indexes outperform the other methods with no edge preservation. The proposed algorithm effectively realizes both despeckling and edge preservation and reaches the state-of-the-art performance.
机译：为了有效地去除散斑时合成孔径雷达（SAR）图像的边缘，本文提出了一种在平移不变的第二代带束变换（TIBT）域中具有边缘检测和模糊聚类的算法。首先利用Canny运算符来检测SAR图像并去除边缘。然后，分别采用TIBT和模糊C均值聚类对边缘去除图像进行分解和去斑。最后，将去除的边缘添加到重建图像中。该算法建议将高频子带中的每个系数作为聚类特征，提出最佳聚类数的计算方法，并在聚类结果中定义信号和噪声。实验结果表明，在没有边缘保留的情况下，视觉质量和评估指标优于其他方法。该算法有效地实现了去斑和边缘保留，并达到了最新的性能。

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