...
  • 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 >Contextual SVM Using Hilbert Space Embedding for Hyperspectral Classification
【24h】

Contextual SVM Using Hilbert Space Embedding for Hyperspectral Classification

机译：使用希尔伯特空间嵌入进行高光谱分类的上下文SVM

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

In this letter, a kernel-based contextual classification approach built on the principle of a newly introduced mapping technique, called Hilbert space embedding, is proposed. The proposed technique, called contextual support vector machine (SVM), is aimed at jointly exploiting both local spectral and spatial information in a reproducing kernel Hilbert space (RKHS) by collectively embedding a set of spectral signatures within a confined local region into a single point in the RKHS that can uniquely represent the corresponding local hyperspectral pixels. Embedding is conducted by calculating the weighted empirical mean of the mapped points in the RKHS to exploit the similarities and variations in the local spectral and spatial information. The weights are adaptively estimated based on the distance between the mapped point in consideration and its neighbors in the RKHS. An SVM separating hyperplane is built to maximize the margin between classes formed by weighted empirical means. The proposed technique showed significant improvement over the composite kernel-based SVM on several hyperspectral images.
机译：在这封信中，提出了一种基于内核的上下文分类方法，该方法基于一种新引入的映射技术原理（称为希尔伯特空间嵌入）构建。所提出的技术称为上下文支持向量机（SVM），旨在通过将一组受限区域内的光谱签名集中嵌入单个点，从而共同利用再生内核希尔伯特空间（RKHS）中的局部光谱和空间信息在RKHS中可以唯一表示相应的局部高光谱像素的图像。嵌入是通过计算RKHS中映射点的加权经验均值来进行的，以利用局部光谱和空间信息的相似性和变化性。权重是根据所考虑的映射点与其在RKHS中相邻点之间的距离来自适应估计的。建立SVM分离超平面可最大程度地增加通过加权经验方法形成的类之间的裕度。所提出的技术在多个高光谱图像上显示出比基于复合核的SVM显着改进。

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