...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Weighted Fusion-Based Representation Classifiers for Marine Floating Raft Detection of SAR Images
【24h】

Weighted Fusion-Based Representation Classifiers for Marine Floating Raft Detection of SAR Images

机译：基于加权融合的SAR图像海洋浮筏表示分类器

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

Detection of a marine floating raft is significant for ocean utilization, which provides a basis for marine ecosystem protection. In this case study, supervised classifiers of weighted fusion-based representation are proposed to detect marine floating raft using synthetic aperture radar images. To remove the speckle noise and obtain more discriminative features, a weighted low-rank matrix factorization (WLRMF) model is developed to optimize features before detection, where the matrix of patch features is decomposed to acquire the denoised features. Weighted fusion-based representation classifiers (WFRCs) with weighted multiplication are proposed to combine the sparse representation classifier (SRC) and the collaborative representation classifier (CRC) for floating raft detection, which can capture the competition between the floating raft and water surface as well as the collaboration within-class samples. Experiments on the study area of the Bohai Sea confirm that the proposed approach produces better results than some related methods. It is demonstrated that the WLRMF model extracts effective features and overcomes the influence of speckle noise at the same time, and the WFRC model is able to take advantages of the SRC in competition and CRC in collaboration for improving detection accuracies.
机译：检测海洋浮筏对于海洋利用具有重要意义，这为保护海洋生态系统提供了基础。在本案例研究中，提出了基于加权融合的表示的监督分类器，以使用合成孔径雷达图像检测海洋浮筏。为了消除斑点噪声并获得更多判别特征，开发了加权低秩矩阵分解（WLRMF）模型，以在检测之前优化特征，在该模型中，将补丁特征矩阵分解以获取降噪特征。提出了基于加权融合的加权融合表示分类器（WFRC），结合稀疏表示分类器（SRC）和协作表示分类器（CRC）进行浮筏检测，也可以捕获浮筏与水面之间的竞争。作为课内合作样本。在渤海研究区进行的实验证实，与某些相关方法相比，该方法产生了更好的结果。结果表明，WLRMF模型能够有效提取特征并克服了斑点噪声的影响，WFRC模型能够在竞争和CRC的结合中利用SRC的优势来提高检测精度。

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